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: "hidden two stream convolutional networks for action recognition"
  Abstract: Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.

Connected through hyperedge HE-1 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "an evaluation of action recognition models on epic kitchens"
    - "large scale weakly supervised pre training for video action recognition"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-2 (induced by source paper "occlusion aware unsupervised learning of optical flow from video"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "temporal reasoning graph for activity recognition"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "linkage based face clustering via graph convolution network"
    - "multi label image recognition with graph convolutional networks"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "graph neural networks a review of methods and applications"
    - "learning discriminative video representations using adversarial perturbations"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "grouped spatial temporal aggregation for efficient action recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "long term feature banks for detailed video understanding"
    - "end to end learning of motion representation for video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "motion feature network fixed motion filter for action recognition"
    - "on the effectiveness of task granularity for transfer learning"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "videos as space time region graphs"

Connected through hyperedge HE-5 (induced by source paper "learning spatio temporal features with two stream deep 3d cnns for lipreading"):
  with members:
    - "deep audio visual speech recognition"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "end to end audiovisual speech recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-6 (induced by source paper "collaborative spatio temporal feature learning for video action recognition"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "spatio temporal channel correlation networks for action classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-7 (induced by source paper "hallucinating optical flow features for video classification"):
  with members:
    - "spatio temporal video re localization by warp lstm"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "non local netvlad encoding for video classification"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "video re localization"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-8 (induced by source paper "self paced video data augmentation with dynamic images generated by generative adversarial networks"):
  with members:
    - "few shot video classification via temporal alignment"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "recycle gan unsupervised video retargeting"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "spectral normalization for generative adversarial networks"
    - "towards high resolution video generation with progressive growing of sliced wasserstein gans"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

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)