human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "consistent generative query networks"):
  Abstract: Stochastic video prediction is usually framed as an extrapolation problem where the goal is to sample a sequence of consecutive future image frames conditioned on a sequence of observed past frames. For the most part, algorithms for this task generate future video frames sequentially in an autoregressive fashion, which is slow and requires the input and output to be consecutive. We introduce a model that overcomes these drawbacks -- it learns to generate a global latent representation from an arbitrary set of frames within a video. This representation can then be used to simultaneously and efficiently sample any number of temporally consistent frames at arbitrary time-points in the video. We apply our model to synthetic video prediction tasks and achieve results that are comparable to state-of-the-art video prediction models. In addition, we demonstrate the flexibility of our model by applying it to 3D scene reconstruction where we condition on location instead of time. To the best of our knowledge, our model is the first to provide flexible and coherent prediction on stochastic video datasets, as well as consistent 3D scene samples. Please check the project website this https URL to view scene reconstructions and videos produced by our model.

Includes members:
  Member "learning and querying fast generative models for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "variational memory addressing in generative models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical quantized autoencoders")
  Member "stochastic variational video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "few shot autoregressive density estimation towards learning to learn distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a network pruning network approach to deep model compression")
  Member "video frame synthesis using deep voxel flow":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "unsupervised learning for physical interaction through video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "3d r2n2 a unified approach for single and multi view 3d object reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "deep predictive coding networks for video prediction and unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")

Question: Please tell me which class the source paper 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)