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: "action temporal localization in untrimmed videos via multi stage cnns"
  Abstract: We address action temporal localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained and contain multiple action instances plus video content of background scenes or other activities. To address this challenging issue, we exploit the effectiveness of deep networks in action temporal localization via multi-stage segment-based 3D ConvNets: (1) a proposal stage identifies candidate segments in a long video that may contain actions; (2) a classification stage learns one-vs-all action classification model to serve as initialization for the localization stage; and (3) a localization stage fine-tunes on the model learnt in the classification stage to localize each action instance. We propose a novel loss function for the localization stage to explicitly consider temporal overlap and therefore achieve high temporal localization accuracy. On two large-scale benchmarks, our approach achieves significantly superior performances compared with other state-of-the-art systems: mAP increases from 1.7% to 7.4% on MEXaction2 and increased from 15.0% to 19.0% on THUMOS 2014, when the overlap threshold for evaluation is set to 0.5.

Connected through hyperedge HE-1 (induced by source paper "probing the state of the art a critical look at visual representation evaluation"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "similarity of neural network representations revisited"
    - "self supervised learning for video correspondence flow"
    - "learning correspondence from the cycle consistency of time"
    - "the visual task adaptation benchmark"
    - "data efficient image recognition with contrastive predictive coding"
    - "revisiting self supervised visual representation learning"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-2 (induced by source paper "interpretable spatio temporal attention for video action recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "human action recognition and prediction a survey"
    - "moments in time dataset one million videos for event understanding"
    - "learn to pay attention"
    - "tell me where to look guided attention inference network"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-3 (induced by source paper "to find where you talk temporal sentence localization in video with attention based location regression"):
  with members:
    - "weakly supervised dense event captioning in videos"
    - "to create what you tell generating videos from captions"
    - "single shot temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "dense captioning events in videos"
    - "tall temporal activity localization via language query"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-4 (induced by source paper "time perception machine temporal point processes for the when where and what of activity prediction"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "modeling and inferring human intents and latent functional objects for trajectory prediction"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"

Connected through hyperedge HE-5 (induced by source paper "extraction and classification of diving clips from continuous video footage"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "end to end learning of action detection from frame glimpses in videos"
    - "beyond short snippets deep networks for video classification"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "online tracking by learning discriminative saliency map with convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "amtnet action micro tube regression by end to end trainable deep architecture"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "actionness estimation using hybrid fully convolutional networks"
    - "convolutional two stream network fusion for video action recognition"
    - "end to end learning of action detection from frame glimpses in videos"
    - "spatial transformer networks"
    - "a robust and efficient video representation for action recognition"
    - "densecap fully convolutional localization networks for dense captioning"

Connected through hyperedge HE-7 (induced by source paper "temporal context network for activity localization in videos"):
  with members:
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "untrimmed video classification for activity detection submission to activitynet challenge"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "improving human action recognition by non action classification"

Connected through hyperedge HE-8 (induced by source paper "the thumos challenge on action recognition for videos in the wild"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "a discriminative cnn video representation for event detection"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"
    - "weakly supervised action labeling in videos under ordering constraints"

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)