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: "slowfast networks for video recognition"
  Abstract: We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA. Code has been made available at: this https URL

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 "multi modal self supervision from generalized data transformations"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "audiovisual slowfast networks for video recognition"
    - "learning spatiotemporal features via video and text pair discrimination"
    - "on mutual information maximization for representation learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"

Connected through hyperedge HE-3 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-4 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "action recognition with spatial temporal discriminative filter banks"
    - "divide and conquer the embedding space for metric learning"
    - "action2vec a crossmodal embedding approach to action learning"
    - "cross and learn cross modal self supervision"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "panda a gigapixel level human centric video dataset"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "widerperson a diverse dataset for dense pedestrian detection in the wild"
    - "group reidentification with multigrained matching and integration"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "vision meets drones a challenge"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"

Connected through hyperedge HE-6 (induced by source paper "multi modal domain adaptation for fine grained action recognition"):
  with members:
    - "temporal attentive alignment for large scale video domain adaptation"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "unsupervised domain adaptation through self supervision"
    - "adversarial cross domain action recognition with co attention"
    - "scaling egocentric vision the epic kitchens dataset"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-7 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "few shot learning via saliency guided hallucination of samples"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "deeply supervised salient object detection with short connections"
    - "learning discriminative video representations using adversarial perturbations"
    - "evolving space time neural architectures for videos"
    - "a deeper look at power normalizations"

Connected through hyperedge HE-8 (induced by source paper "evolving losses for unsupervised video representation learning"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "moments in time dataset one million videos for event understanding"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised learning via meta learning"
    - "autoaugment learning augmentation policies from data"
    - "self supervised video representation learning with space time cubic puzzles"

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)