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: "rethinking the faster r cnn architecture for temporal action localization"
  Abstract: We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key shortcomings of existing approaches: (1) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations; (2) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields; and (3) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important. We achieve state-of-the-art performance for both action proposal and localization on THUMOS'14 detection benchmark and competitive performance on ActivityNet challenge.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised temporal action localization using deep metric learning"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"

Connected through hyperedge HE-2 (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"
    - "slowfast networks for video recognition"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "bsn boundary sensitive network for temporal action proposal generation"

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"
    - "densepose dense human pose estimation in the wild"

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 "why can t i dance in the mall learning to mitigate scene bias in action recognition"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "bias resilient neural network"
    - "long term feature banks for detailed video understanding"
    - "women also snowboard overcoming bias in captioning models"
    - "algorithms and theory for multiple source adaptation"
    - "dropblock a regularization method for convolutional networks"
    - "learning not to learn training deep neural networks with biased data"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

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 "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)