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: "temporal action detection with structured segment networks"
  Abstract: Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we present the structured segment network (SSN), a novel framework which models the temporal structure of each action instance via a structured temporal pyramid. On top of the pyramid, we further introduce a decomposed discriminative model comprising two classifiers, respectively for classifying actions and determining completeness. This allows the framework to effectively distinguish positive proposals from background or incomplete ones, thus leading to both accurate recognition and localization. These components are integrated into a unified network that can be efficiently trained in an end-to-end fashion. Additionally, a simple yet effective temporal action proposal scheme, dubbed temporal actionness grouping (TAG) is devised to generate high quality action proposals. On two challenging benchmarks, THUMOS14 and ActivityNet, our method remarkably outperforms previous state-of-the-art methods, demonstrating superior accuracy and strong adaptivity in handling actions with various temporal structures.

Connected through hyperedge HE-1 (induced by source paper "learning spatiotemporal features via video and text pair discrimination"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "contrastive multiview coding"
    - "universal dependency parsing from scratch"
    - "dynamonet dynamic action and motion network"
    - "learning correspondence from the cycle consistency of time"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"

Connected through hyperedge HE-2 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph modules for modeling object object interactions in activity recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised temporal action localization using deep metric learning"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "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"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"

Connected through hyperedge HE-4 (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-5 (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-6 (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"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-7 (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-8 (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"
    - "bsn boundary sensitive network for 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"

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)