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: "depth2action exploring embedded depth for large scale action recognition"
  Abstract: This paper performs the first investigation into depth for large-scale human action recognition in video where the depth cues are estimated from the videos themselves. We develop a new framework called depth2action and experiment thoroughly into how best to incorporate the depth information. We introduce spatio-temporal depth normalization (STDN) to enforce temporal consistency in our estimated depth sequences. We also propose modified depth motion maps (MDMM) to capture the subtle temporal changes in depth. These two components significantly improve the action recognition performance. We evaluate our depth2action framework on three large-scale action recognition video benchmarks. Our model achieves state-of-the-art performance when combined with appearance and motion information thus demonstrating that depth2action is indeed complementary to existing approaches.

Connected through hyperedge HE-1 (induced by source paper "action recognition in untrimmed videos with composite self attention two stream framework"):
  with members:
    - "learning transferable self attentive representations for action recognition in untrimmed videos with weak supervision"
    - "end to end learning of motion representation for video understanding"
    - "towards universal representation for unseen action recognition"
    - "transductive unbiased embedding for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "combined static and motion features for deep networks based activity recognition in videos"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "beyond short snippets deep networks for video classification"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "finding action tubes"
    - "two stream convolutional networks for action recognition in videos"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "alternative semantic representations for zero shot human action recognition"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"
    - "guided optical flow learning"
    - "image de raining using a conditional generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "fine grained land use classification at the city scale using ground level images"):
  with members:
    - "building instance classification using street view images"
    - "large scale mapping of human activity using geo tagged videos"
    - "hidden two stream convolutional networks for action recognition"
    - "quantitative comparison of open source data for fine grain mapping of land use"
    - "webvision challenge visual learning and understanding with web data"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "adaptive deep pyramid matching for remote sensing scene classification"

Connected through hyperedge HE-5 (induced by source paper "gated transfer network for transfer learning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep texture manifold for ground terrain recognition"
    - "towards universal representation for unseen action recognition"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "random temporal skipping for multirate video analysis"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "towards universal representation for unseen action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep local video feature for action recognition"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"

Connected through hyperedge HE-7 (induced by source paper "deep texture manifold for ground terrain recognition"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "exemplar centered supervised shallow parametric data embedding"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "hidden two stream convolutional networks for action recognition"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "deep ten texture encoding network"
    - "a 4d light field dataset and cnn architectures for material recognition"

Connected through hyperedge HE-8 (induced by source paper "learning optical flow via dilated networks and occlusion reasoning"):
  with members:
    - "video enhancement with task oriented flow"
    - "hidden two stream convolutional networks for action recognition"
    - "occlusion aware unsupervised learning of optical flow"
    - "dilated residual networks"
    - "densenet for dense flow"
    - "guided optical flow learning"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

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)