human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
  Abstract: Abstract   In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object’s orientation and on a sensor’s flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters.

Connected through hyperedge HE-1 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "object contextual representations for semantic segmentation"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-2 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf"):
  with members:
    - "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "pixel adaptive convolutional neural networks"
    - "relation network for multi label aerial image classification"
    - "building footprint generation using improved generative adversarial networks"
    - "building segmentation through a gated graph convolutional neural network with deep structured feature embedding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"

Connected through hyperedge HE-3 (induced by source paper "general e 2 equivariant steerable cnns"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning to convolve a generalized weight tying approach"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-4 (induced by source paper "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "switchable temporal propagation network"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning affinity via spatial propagation networks"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "relation network for multi label aerial image classification"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution"
    - "recent advances and opportunities in scene classification of aerial images with deep models"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "fine grained object recognition and zero shot learning in remote sensing imagery"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"
    - "relation networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "remote sensing image scene classification benchmark and state of the art"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised semantic segmentation of satellite images"):
  with members:
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "exploiting saliency for object segmentation from image level labels"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-8 (induced by source paper "isaid a large scale dataset for instance segmentation in aerial images"):
  with members:
    - "spacenet mvoi a multi view overhead imagery dataset"
    - "xview objects in context in overhead imagery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "dota a large scale dataset for object detection in aerial images"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)