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: "exploring temporal preservation networks for precise temporal action localization"
  Abstract: Temporal action localization is an important task of computer vision. Though a variety of methods have been proposed, it still remains an open question how to predict the temporal boundaries of action segments precisely. Most works use segment-level classifiers to select video segments pre-determined by action proposal or dense sliding windows. However, in order to achieve more precise action boundaries, a temporal localization system should make dense predictions at a fine granularity. A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization. However, CDC network loses temporal information partially due to the temporal downsampling operation. In this paper, we propose an elegant and powerful Temporal Preservation Convolutional (TPC) Network that equips 3D ConvNets with TPC filters. TPC network can fully preserve temporal resolution and downsample the spatial resolution simultaneously, enabling frame-level granularity action localization. TPC network can be trained in an end-to-end manner. Experiment results on public datasets show that TPC network achieves significant improvement on per-frame action prediction and competing results on segment-level temporal action localization.

Connected through hyperedge HE-1 (induced by source paper "srg snippet relatedness based temporal action proposal generator"):
  with members:
    - "learning blind video temporal consistency"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "cbam convolutional block attention module"
    - "precise temporal action localization by evolving temporal proposals"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "adversarial seeded sequence growing for weakly supervised temporal action localization"):
  with members:
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "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"

Connected through hyperedge HE-3 (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"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "exploring frame segmentation networks for temporal action localization"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal segment networks for action recognition in videos"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "the thumos challenge on action recognition for videos in the wild"
    - "temporal action localization in untrimmed videos via multi stage cnns"

Connected through hyperedge HE-5 (induced by source paper "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to separate object sounds by watching unlabeled video"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "instance similarity deep hashing for multi label image retrieval"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "single shot temporal action detection"

Connected through hyperedge HE-6 (induced by source paper "a proposal based solution to spatio temporal action detection in untrimmed videos"):
  with members:
    - "ctap complementary temporal action proposal generation"
    - "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"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-7 (induced by source paper "precise temporal action localization by evolving temporal proposals"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "non local neural networks"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-8 (induced by source paper "blp boundary likelihood pinpointing networks for accurate temporal action localization"):
  with members:
    - "diagnosing error in temporal action detectors"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "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"
    - "compressed video action recognition"
    - "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)