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: "end to end learning of motion representation for video understanding"
  Abstract: Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks. To fill this gap, we propose TVNet, a novel end-to-end trainable neural network, to learn optical-flow-like features from data. TVNet subsumes a specific optical flow solver, the TV-L1 method, and is initialized by unfolding its optimization iterations as neural layers. TVNet can therefore be used directly without any extra learning. Moreover, it can be naturally concatenated with other task-specific networks to formulate an end-to-end architecture, thus making our method more efficient than current multi-stage approaches by avoiding the need to pre-compute and store features on disk. Finally, the parameters of the TVNet can be further fine-tuned by end-to-end training. This enables TVNet to learn richer and task-specific patterns beyond exact optical flow. Extensive experiments on two action recognition benchmarks verify the effectiveness of the proposed approach. Our TVNet achieves better accuracies than all compared methods, while being competitive with the fastest counterpart in terms of features extraction time.

Connected through hyperedge HE-1 (induced by source paper "dmc net generating discriminative motion cues for fast compressed video action recognition"):
  with members:
    - "multi fiber networks for video recognition"
    - "contextual multi scale region convolutional 3d network for activity detection"
    - "stochastic adversarial video prediction"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "compressed video action recognition"

Connected through hyperedge HE-2 (induced by source paper "video modeling with correlation networks"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "motion feature network fixed motion filter for action recognition"
    - "using phase instead of optical flow for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "representation flow for action recognition"
    - "learning discriminative motion features through detection"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-3 (induced by source paper "paying more attention to motion attention distillation for learning video representations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deeply supervised salient object detection with short connections"
    - "what have we learned from deep representations for action recognition"
    - "on the effectiveness of task granularity for transfer learning"
    - "end to end multi task learning with attention"
    - "modality distillation with multiple stream networks for action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "the sound of motions"):
  with members:
    - "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
    - "self supervised audio visual co segmentation"
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-5 (induced by source paper "learning efficient video representation with video shuffle networks"):
  with members:
    - "learning video representations from correspondence proposals"
    - "collaborative spatio temporal feature learning for video action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "videos as space time region graphs"
    - "representation flow for action recognition"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-6 (induced by source paper "learning video representations from correspondence proposals"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "flownet3d learning scene flow in 3d point clouds"
    - "motion feature network fixed motion filter for action recognition"
    - "deep sets"
    - "dynamic graph cnn for learning on point clouds"
    - "motion fused frames data level fusion strategy for hand gesture recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"

Connected through hyperedge HE-7 (induced by source paper "graph convolutional networks for temporal action localization"):
  with members:
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "weakly supervised dense event captioning in videos"
    - "person re identification with deep similarity guided graph neural network"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "videos as space time region graphs"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-8 (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"
    - "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"
    - "slowfast networks for video recognition"

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)