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: "unsupervised learning using sequential verification for action recognition"
  Abstract: In this paper, we consider the problem of learning a visual representation from the raw spatiotemporal signals in videos for use in action recognition. Our representation is learned without supervision from semantic labels. We formulate it as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful unsupervised representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. Our method can also be combined with supervised representations to provide an additional boost in accuracy for action recognition. Finally, to quantify its sensitivity to human pose, we show results for human pose estimation on the FLIC dataset that are competitive with approaches using significantly more supervised training data.

Connected through hyperedge HE-1 (induced by source paper "regularizing deep networks by modeling and predicting label structure"):
  with members:
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "colorization as a proxy task for visual understanding"
    - "learning representations for automatic colorization"
    - "multigrid neural architectures"
    - "ambient sound provides supervision for visual learning"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "improving spatiotemporal self supervision by deep reinforcement learning"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "unsupervised representation learning by predicting image rotations"
    - "deep unsupervised learning of visual similarities"
    - "deeppermnet visual permutation learning"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"

Connected through hyperedge HE-3 (induced by source paper "cross and learn cross modal self supervision"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep unsupervised learning of visual similarities"
    - "colorization as a proxy task for visual understanding"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "deep multimodal representation learning from temporal data"
    - "the kinetics human action video dataset"
    - "unsupervised representation learning by sorting sequences"

Connected through hyperedge HE-4 (induced by source paper "colorization as a proxy task for visual understanding"):
  with members:
    - "learning representations for automatic colorization"
    - "ambient sound provides supervision for visual learning"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "adversarial feature learning"
    - "learning features by watching objects move"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "split brain autoencoders unsupervised learning by cross channel prediction"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-5 (induced by source paper "self supervised video representation learning with odd one out networks"):
  with members:
    - "rank pooling for action recognition"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "action recognition with dynamic image networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"
    - "videolstm convolves attends and flows for action recognition"
    - "unsupervised visual representation learning by context prediction"
    - "exploiting image trained cnn architectures for unconstrained video classification"

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)