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: "video time properties encoders and evaluation"
  Abstract: Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding. While many attempted to model time in videos, an explicit study on quantifying video time is missing. To fill this lacuna, we aim to evaluate video time explicitly. We describe three properties of video time, namely a) temporal asymmetry, b)temporal continuity and c) temporal causality. Based on each we formulate a task able to quantify the associated property. This allows assessing the effectiveness of modern video encoders, like C3D and LSTM, in their ability to model time. Our analysis provides insights about existing encoders while also leading us to propose a new video time encoder, which is better suited for the video time recognition tasks than C3D and LSTM. We believe the proposed meta-analysis can provide a reasonable baseline to assess video time encoders on equal grounds on a set of temporal-aware tasks.

Connected through hyperedge HE-1 (induced by source paper "pic permutation invariant convolution for recognizing long range activities"):
  with members:
    - "videograph recognizing minutes long human activities in videos"
    - "large memory layers with product keys"
    - "long term feature banks for detailed video understanding"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "video action transformer network"
    - "timeception for complex action recognition"
    - "zero shot anticipation for instructional activities"

Connected through hyperedge HE-2 (induced by source paper "videograph recognizing minutes long human activities in videos"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "video action transformer network"
    - "timeception for complex action recognition"

Connected through hyperedge HE-3 (induced by source paper "retro actions learning close by time reversing open videos"):
  with members:
    - "action completion a temporal model for moment detection"
    - "time reversal as self supervision"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "slowfast networks for video recognition"
    - "video jigsaw unsupervised learning of spatiotemporal context for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "interpreting video features a comparison of 3d convolutional networks and convolutional lstm networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "what have we learned from deep representations for action recognition"
    - "methods for interpreting and understanding deep neural networks"
    - "sanity checks for saliency maps"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "excitation backprop for rnns"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-5 (induced by source paper "timeception for complex action recognition"):
  with members:
    - "what have we learned from deep representations for action recognition"
    - "videos as space time region graphs"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "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"

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)