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: "bsn boundary sensitive network for temporal action proposal generation"
  Abstract: Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content. This problem requires methods not only generating proposals with precise temporal boundaries, but also retrieving proposals to cover truth action instances with high recall and high overlap using relatively fewer proposals. To address these difficulties, we introduce an effective proposal generation method, named Boundary-Sensitive Network (BSN), which adopts "local to global" fashion. Locally, BSN first locates temporal boundaries with high probabilities, then directly combines these boundaries as proposals. Globally, with Boundary-Sensitive Proposal feature, BSN retrieves proposals by evaluating the confidence of whether a proposal contains an action within its region. We conduct experiments on two challenging datasets: ActivityNet-1.3 and THUMOS14, where BSN outperforms other state-of-the-art temporal action proposal generation methods with high recall and high temporal precision. Finally, further experiments demonstrate that by combining existing action classifiers, our method significantly improves the state-of-the-art temporal action detection performance.

Connected through hyperedge HE-1 (induced by source paper "sf net single frame supervision for temporal action localization"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "3c net category count and center loss for weakly supervised action localization"
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "weakly supervised action localization with background modeling"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "slowfast networks for video recognition"
    - "weakly supervised action segmentation with iterative soft boundary assignment"

Connected through hyperedge HE-2 (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"
    - "soft nms improving object detection with one line of code"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised multi level attentional reconstruction network for grounding textual queries in videos"):
  with members:
    - "look closer to ground better weakly supervised temporal grounding of sentence in video"
    - "semantic conditioned dynamic modulation for temporal sentence grounding in videos"
    - "weakly supervised video moment retrieval from text queries"
    - "tripping through time efficient localization of activities in videos"
    - "temporally grounding language queries in videos by contextual boundary aware prediction"
    - "bmn boundary matching network for temporal action proposal generation"
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "multilevel language and vision integration for text to clip retrieval"

Connected through hyperedge HE-4 (induced by source paper "accurate temporal action proposal generation with relation aware pyramid network"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "reconstruction network for video captioning"
    - "soft nms improving object detection with one line of code"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "refineloc iterative refinement for weakly supervised action localization"):
  with members:
    - "3c net category count and center loss for weakly supervised action localization"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "unsupervised pre training of image features on non curated data"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised action localization with background modeling"
    - "bmn boundary matching network for temporal action proposal generation"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-6 (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-7 (induced by source paper "scsampler sampling salient clips from video for efficient action recognition"):
  with members:
    - "video classification with channel separated convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "learning to separate object sounds by watching unlabeled video"
    - "adaframe adaptive frame selection for fast video recognition"
    - "slowfast networks for video recognition"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-8 (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"

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)