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: "untrimmed video classification for activity detection submission to activitynet challenge"
  Abstract: Current state-of-the-art human activity recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame. We propose a simple, yet effective, method for the temporal detection of activities in temporally untrimmed videos with the help of untrimmed classification. Firstly, our model predicts the top k labels for each untrimmed video by analysing global video-level features. Secondly, frame-level binary classification is combined with dynamic programming to generate the temporally trimmed activity proposals. Finally, each proposal is assigned a label based on the global label, and scored with the score of the temporal activity proposal and the global score. Ultimately, we show that untrimmed video classification models can be used as stepping stone for temporal detection.

Connected through hyperedge HE-1 (induced by source paper "g tad sub graph localization for temporal action detection"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "learning video representations from correspondence proposals"
    - "graph convolutional networks for temporal action localization"
    - "mesh r cnn"
    - "bmn boundary matching network for temporal action proposal generation"
    - "deepgcns can gcns go as deep as cnns"
    - "point clouds learning with attention based graph convolution networks"
    - "diagnosing error in temporal action detectors"

Connected through hyperedge HE-2 (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"
    - "activitynet challenge 2017 summary"
    - "single shot temporal action detection"

Connected through hyperedge HE-3 (induced by source paper "marginalized average attentional network for weakly supervised learning"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "structured attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "temporal action detection with structured segment networks"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-4 (induced by source paper "two stream region convolutional 3d network for temporal activity detection"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-5 (induced by source paper "graph convolutional networks for temporal action localization"):
  with members:
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "end to end learning of motion representation for video understanding"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "weakly supervised dense event captioning in videos"
    - "person re identification with deep similarity guided graph neural network"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "videos as space time region graphs"

Connected through hyperedge HE-6 (induced by source paper "adapnet adaptability decomposing encoder decoder network for weakly supervised action recognition and localization"):
  with members:
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "modeling multimodal clues in a hybrid deep learning framework for video classification"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-7 (induced by source paper "temporal action localization using long short term dependency"):
  with members:
    - "weakly supervised visual instrument playing action detection in videos"
    - "w talc weakly supervised temporal activity localization and classification"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "compressed video action recognition"
    - "single shot temporal action detection"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-8 (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"
    - "activitynet challenge 2017 summary"
    - "single shot temporal action detection"

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)