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: "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"
  Abstract: In this paper we tackle a very novel problem, namely height estimation from a single monocular remote sensing image, which is inherently ambiguous, and a technically ill-posed problem, with a large source of uncertainty coming from the overall scale. We propose a fully convolutional-deconvolutional network architecture being trained end-to-end, encompassing residual learning, to model the ambiguous mapping between monocular remote sensing images and height maps. Specifically, it is composed of two parts, i.e., convolutional sub-network and deconvolutional sub-network. The former corresponds to feature extractor that transforms the input remote sensing image to high-level multidimensional feature representation, whereas the latter plays the role of a height generator that produces height map from the feature extracted from the convolutional sub-network. Moreover, to preserve fine edge details of estimated height maps, we introduce a skip connection to the network, which is able to shuttle low-level visual information, e.g., object boundaries and edges, directly across the network. To demonstrate the usefulness of single-view height prediction, we show a practical example of instance segmentation of buildings using estimated height map. This paper, for the first time in the remote sensing community, attempts to estimate height from monocular vision. The proposed network is validated using a large-scale high resolution aerial image data set covered an area of Berlin. Both visual and quantitative analysis of the experimental results demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (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"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "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"
    - "relation networks for object detection"

Connected through hyperedge HE-2 (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"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate 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"
    - "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-3 (induced by source paper "multi task learning of height and semantics from aerial images"):
  with members:
    - "multi task learning as multi objective optimization"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeper depth prediction with fully convolutional residual networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "semantic stereo for incidental satellite images"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "segstereo exploiting semantic information for disparity estimation"
    - "dispsegnet leveraging semantics for end to end learning of disparity estimation from stereo imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "3d geometry aware semantic labeling of outdoor street scenes"
    - "fast disparity estimation using dense networks"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "memory efficient implementation of densenets"

Connected through hyperedge HE-5 (induced by source paper "overcoming missing and incomplete modalities with generative adversarial networks for building footprint segmentation"):
  with members:
    - "adversarial defense based on structure to signal autoencoders"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"

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)