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 "high fidelity video prediction with large stochastic recurrent neural networks"):
  Abstract: Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time. Previously proposed solutions require complex inductive biases inside network architectures with highly specialized computation, including segmentation masks, optical flow, and foreground and background separation. In this work, we question if such handcrafted architectures are necessary and instead propose a different approach: finding minimal inductive bias for video prediction while maximizing network capacity. We investigate this question by performing the first large-scale empirical study and demonstrate state-of-the-art performance by learning large models on three different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling car driving.

Includes members:
  Member "model based reinforcement learning for atari":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "videoflow a flow based generative model for video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi variate probabilistic time series forecasting via conditioned normalizing flows")
  Member "hierarchical long term video prediction without supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic latent residual video prediction")
  Member "learning latent dynamics for planning from pixels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "mt vae learning motion transformations to generate multimodal human dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "visual foresight model based deep reinforcement learning for vision based robotic control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "stochastic video generation with a learned prior":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "gpipe efficient training of giant neural networks using pipeline parallelism":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)