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 "transformation based adversarial video prediction on large scale data"):
  Abstract: Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video. In this work, we focus on the task of video prediction, where given a sequence of frames extracted from a video, the goal is to generate a plausible future sequence. We first improve the state of the art by performing a systematic empirical study of discriminator decompositions and proposing an architecture that yields faster convergence and higher performance than previous approaches. We then analyze recurrent units in the generator, and propose a novel recurrent unit which transforms its past hidden state according to predicted motion-like features, and refines it to to handle dis-occlusions, scene changes and other complex behavior. We show that this recurrent unit consistently outperforms previous designs. Our final model leads to a leap in the state-of-the-art performance, obtaining a test set Frechet Video Distance of 25.7, down from 69.2, on the large-scale Kinetics-600 dataset.

Includes members:
  Member "a short note on the kinetics 700 human action dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "large scale adversarial representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "high fidelity speech synthesis with adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "waveflow a compact flow based model for raw audio")
  Member "adversarial video generation on complex datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "scaling autoregressive video models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic latent residual video prediction")
  Member "object oriented dynamics predictor":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised state representation learning in atari")
  Member "tganv2 efficient training of large models for video generation with multiple subsampling layers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial video generation on complex datasets")
  Member "sequential attend infer repeat generative modelling of moving objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")

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)