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: "improving human action recognition by non action classification"
  Abstract: In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors. We first study the benefits of removing non-action video segments, which are the ones that do not portray any human action. We then learn a non-action classifier and use it to down-weight irrelevant video segments. The non-action classifier is trained using ActionThread, a dataset with shot-level annotation for the occurrence or absence of a human action. The non-action classifier can be used to identify non-action shots with high precision and subsequently used to improve the performance of action recognition systems.

Connected through hyperedge HE-1 (induced by source paper "temporal context network for activity localization in videos"):
  with members:
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "untrimmed video classification for activity detection submission to activitynet challenge"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "action temporal localization in untrimmed videos via multi stage cnns"

Connected through hyperedge HE-2 (induced by source paper "efficient action detection in untrimmed videos via multi task learning"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "depth2action exploring embedded depth for large scale action recognition"
    - "dap3d net where what and how actions occur in videos"
    - "end to end learning of action detection from frame glimpses in videos"
    - "instance aware semantic segmentation via multi task network cascades"
    - "deep residual learning for image recognition"
    - "deepstereo learning to predict new views from the world s imagery"
    - "objects2action classifying and localizing actions without any video example"

Connected through hyperedge HE-3 (induced by source paper "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"):
  with members:
    - "rank pooling for action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "videolstm convolves attends and flows for action recognition"
    - "discriminatively trained latent ordinal model for video classification"
    - "unsupervised learning of video representations using lstms"
    - "a robust and efficient video representation for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "action recognition using visual attention"

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)