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: "beyond short snippets deep networks for video classification"
  Abstract: Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first method explores various convolutional temporal feature pooling architectures, examining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improvements over previously published results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.6% vs. 88.0%) and without additional optical flow information (82.6% vs. 72.8%).

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 "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "multi fiber networks for video recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "modality compensation network cross modal adaptation for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "temporal bilinear networks for video action recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "pku mmd a large scale benchmark for continuous multi modal human action understanding"

Connected through hyperedge HE-5 (induced by source paper "videossl semi supervised learning for video classification"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "only time can tell discovering temporal data for temporal modeling"
    - "distinit learning video representations without a single labeled video"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "video representation learning by dense predictive coding"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "semi supervised convolutional neural networks for human activity recognition"

Connected through hyperedge HE-6 (induced by source paper "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"):
  with members:
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "connectionist temporal modeling for weakly supervised action labeling"

Connected through hyperedge HE-7 (induced by source paper "v4d 4d convolutional neural networks for video level representation learning"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning video representations from correspondence proposals"
    - "eco efficient convolutional network for online video understanding"
    - "action4d real time action recognition in the crowd and clutter"
    - "compact generalized non local network"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-8 (induced by source paper "3d gated recurrent fusion for semantic scene completion"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "adaptive fusion for rgb d salient object detection"
    - "efficient semantic scene completion network with spatial group convolution"
    - "scalable place recognition under appearance change for autonomous driving"
    - "two stream 3d semantic scene completion"
    - "unsupervised rgbd video object segmentation using gans"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

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)