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: "activitynet challenge 2017 summary"
  Abstract: The ActivityNet Large Scale Activity Recognition Challenge 2017 Summary: results and challenge participants papers.

Connected through hyperedge HE-1 (induced by source paper "constraining temporal relationship for action localization"):
  with members:
    - "scale matters temporal scale aggregation network for precise action localization in untrimmed videos"
    - "graph convolutional networks for temporal action localization"
    - "cornernet detecting objects as paired keypoints"
    - "moments in time dataset one million videos for event understanding"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-2 (induced by source paper "knowledge integration networks for action recognition"):
  with members:
    - "geometric pose affordance 3d human pose with scene constraints"
    - "eco efficient convolutional network for online video understanding"
    - "temporal bilinear networks for video action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "actor action semantic segmentation with region masks"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "scale matters temporal scale aggregation network for precise action localization in untrimmed videos"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "end to end dense video captioning with masked transformer"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "ctap complementary temporal action proposal generation"

Connected through hyperedge HE-4 (induced by source paper "deep concept wise temporal convolutional networks for action localization"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-5 (induced by source paper "bmn boundary matching network for temporal action proposal generation"):
  with members:
    - "decoupling localization and classification in single shot temporal action detection"
    - "ctap complementary temporal action proposal generation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-6 (induced by source paper "seeing and hearing egocentric actions how much can we learn"):
  with members:
    - "fbk hupba submission to the epic kitchens 2019 action recognition challenge"
    - "uncertainty aware audiovisual activity recognition using deep bayesian variational inference"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "audio based activities of daily living adl recognition with large scale acoustic embeddings from online videos"
    - "how much does audio matter to recognize egocentric object interactions"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "lsta long short term attention for egocentric action recognition"

Connected through hyperedge HE-7 (induced by source paper "exploring feature representation and training strategies in temporal action localization"):
  with members:
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "turn tap temporal unit regression network for temporal action proposals"

Connected through hyperedge HE-8 (induced by source paper "a high performance computing method for accelerating temporal action proposal generation"):
  with members:
    - "aed net an abnormal event detection network"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "temporal action detection with structured segment networks"
    - "temporal context network for activity localization in videos"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "convolutional two stream network fusion for video action recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)