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: "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
  Abstract: This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only supervision of video-level labels and makes prediction of intervals of multiple actions. Specifically, we first assemble video clips according to class labels by an attention mechanism that learns class-variable attention weights and thus helps the noise relieving from background or other actions. Secondly, we build temporal relationship between actions by feeding the assembled features into an enhanced recurrent neural network. Finally, we transform the output of recurrent neural network into the corresponding action distribution. In order to generate more precise temporal proposals, we design a score term called segregated temporal gradient-weighted class activation mapping (ST-GradCAM) fused with attention weights. Experiments on THUMOS'14 and ActivityNet1.3 datasets show that our approach outperforms the state-of-the-art weakly-supervised method, and performs at par with the fully-supervised counterparts.

Connected through hyperedge HE-1 (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"
    - "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"
    - "temporal gaussian mixture layer for videos"

Connected through hyperedge HE-2 (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"
    - "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"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "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"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial seeded sequence growing for weakly supervised temporal action localization"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "step by step erasion one by one collection a weakly supervised temporal action detector"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-5 (induced by source paper "background suppression network for weakly supervised temporal action localization"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "gaussian temporal awareness networks for action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

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)