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: "turn tap temporal unit regression network for temporal action proposals"
  Abstract: Temporal Action Proposal (TAP) generation is an important problem, as fast and accurate extraction of semantically important (e.g. human actions) segments from untrimmed videos is an important step for large-scale video analysis. We propose a novel Temporal Unit Regression Network (TURN) model. There are two salient aspects of TURN: (1) TURN jointly predicts action proposals and refines the temporal boundaries by temporal coordinate regression; (2) Fast computation is enabled by unit feature reuse: a long untrimmed video is decomposed into video units, which are reused as basic building blocks of temporal proposals. TURN outperforms the state-of-the-art methods under average recall (AR) by a large margin on THUMOS-14 and ActivityNet datasets, and runs at over 880 frames per second (FPS) on a TITAN X GPU. We further apply TURN as a proposal generation stage for existing temporal action localization pipelines, it outperforms state-of-the-art performance on THUMOS-14 and ActivityNet.

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 "zstad zero shot temporal activity detection"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "towards universal representation for unseen action recognition"
    - "zero shot object detection by hybrid region embedding"
    - "w talc weakly supervised temporal activity localization and classification"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "similarity r c3d for few shot temporal activity detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-3 (induced by source paper "follow the attention combining partial pose and object motion for fine grained action detection"):
  with members:
    - "skeleton based action recognition of people handling objects"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-4 (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-5 (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-6 (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-7 (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-8 (induced by source paper "proposal free temporal moment localization of a natural language query in video using guided attention"):
  with members:
    - "tripping through time efficient localization of activities in videos"
    - "excl extractive clip localization using natural language descriptions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "diagnosing error in temporal action detectors"
    - "mac mining activity concepts for language based temporal localization"
    - "multilevel language and vision integration for text to clip retrieval"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "to find where you talk temporal sentence localization in video with attention based location regression"

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)