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: "high resolution aerial image labeling with convolutional neural networks"
  Abstract: The problem of dense semantic labeling consists in assigning semantic labels to  every  pixel in an image. In the context of aerial image analysis, it is particularly important to yield high-resolution outputs. In order to use convolutional neural networks (CNNs) for this task, it is required to design new specific architectures to provide fine-grained classification maps. Many dense semantic labeling CNNs have been recently proposed. Our first contribution is an in-depth analysis of these architectures. We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties. We observe that even though they provide competitive results, these CNNs often underexploit properties of semantic labeling that could lead to more effective and efficient architectures. Out of these observations, we then derive a CNN framework specifically adapted to the semantic labeling problem. In addition to learning features at different resolutions, it learns how to combine these features. By integrating local and global information in an efficient and flexible manner, it outperforms previous techniques. We evaluate the proposed framework and compare it with state-of-the-art architectures on public benchmarks of high-resolution aerial image labeling.

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"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks"):
  with members:
    - "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "multimodal unsupervised image to image translation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "ternausnetv2 fully convolutional network for instance segmentation"

Connected through hyperedge HE-3 (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"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "generalizing deep models for overhead image segmentation through getis ord gi pooling"):
  with members:
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "dv3 hed a dcnns based framework to monitor temporary works and esas in railway construction project using vhr satellite images"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "land cover classification with high resolution remote sensing images using transferable deep models"):
  with members:
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "texture characterization using shape co occurrence patterns"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "exploiting deep features for remote sensing image retrieval a systematic investigation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-7 (induced by source paper "an entropic optimal transport loss for learning deep neural networks under label noise in remote sensing images"):
  with members:
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a semi supervised two stage approach to learning from noisy labels"
    - "high resolution multispectral dataset for semantic segmentation"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning aerial image segmentation from online maps"

Connected through hyperedge HE-8 (induced by source paper "deep multi task learning for a geographically regularized semantic segmentation of aerial images"):
  with members:
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "densely connected convolutional networks"
    - "classification with an edge improving semantic image segmentation with boundary detection"
    - "holistically nested edge detection"

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)