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 of view invariant action representations"
  Abstract: The recent success in human action recognition with deep learning methods mostly adopt the supervised learning paradigm, which requires significant amount of manually labeled data to achieve good performance. However, label collection is an expensive and time-consuming process. In this work, we propose an unsupervised learning framework, which exploits unlabeled data to learn video representations. Different from previous works in video representation learning, our unsupervised learning task is to predict 3D motion in multiple target views using video representation from a source view. By learning to extrapolate cross-view motions, the representation can capture view-invariant motion dynamics which is discriminative for the action. In addition, we propose a view-adversarial training method to enhance learning of view-invariant features. We demonstrate the effectiveness of the learned representations for action recognition on multiple datasets.

Connected through hyperedge HE-1 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "ffnet video fast forwarding via reinforcement learning"
    - "predicting visual features from text for image and video caption retrieval"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "improved image captioning via policy gradient optimization of spider"
    - "dual glance model for deciphering social relationships"
    - "summarization of user generated sports video by using deep action recognition features"
    - "attention transfer from web images for video recognition"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "multi modal domain adaptation for fine grained action recognition"):
  with members:
    - "temporal attentive alignment for large scale video domain adaptation"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "unsupervised domain adaptation through self supervision"
    - "adversarial cross domain action recognition with co attention"
    - "scaling egocentric vision the epic kitchens dataset"
    - "slowfast networks for video recognition"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised multi person action recognition in 360 circ videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improved person detection on omnidirectional images with non maxima suppression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dual glance model for deciphering social relationships"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-4 (induced by source paper "synthetic humans for action recognition from unseen viewpoints"):
  with members:
    - "temporally coherent full 3d mesh human pose recovery from monocular video"
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning to train with synthetic humans"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "virtualhome simulating household activities via programs"
    - "human action recognition and prediction a survey"

Connected through hyperedge HE-5 (induced by source paper "action recognition using volumetric motion representations"):
  with members:
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "unsupervised learning of long term motion dynamics for videos"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "view invariant probabilistic embedding for human pose"):
  with members:
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "vehicle re identification with viewpoint aware metric learning"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "unsupervised geometry aware representation for 3d human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "predict cluster unsupervised skeleton based action recognition"):
  with members:
    - "clustering and recognition of spatiotemporal features through interpretable embedding of sequence to sequence recurrent neural networks"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "unsupervised learning of long term motion dynamics for videos"
    - "on human motion prediction using recurrent neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-8 (induced by source paper "time aware and view aware video rendering for unsupervised representation learning"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "adversarial framework for unsupervised learning of motion dynamics in videos"
    - "improving video generation for multi functional applications"
    - "decomposing motion and content for natural video sequence prediction"
    - "contextvp fully context aware video prediction"
    - "stochastic variational video prediction"
    - "dynamics transfer gan generating video by transferring arbitrary temporal dynamics from a source video to a single target image"
    - "mocogan decomposing motion and content for video generation"

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)