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: "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
  Abstract: We introduce a new encoder-decoder GAN model, FutureGAN, that predicts future frames of a video sequence conditioned on a sequence of past frames. During training, the networks solely receive the raw pixel values as an input, without relying on additional constraints or dataset specific conditions. To capture both the spatial and temporal components of a video sequence, spatio-temporal 3d convolutions are used in all encoder and decoder modules. Further, we utilize concepts of the existing progressively growing GAN (PGGAN) that achieves high-quality results on generating high-resolution single images. The FutureGAN model extends this concept to the complex task of video prediction. We conducted experiments on three different datasets, MovingMNIST, KTH Action, and Cityscapes. Our results show that the model learned representations to transform the information of an input sequence into a plausible future sequence effectively for all three datasets. The main advantage of the FutureGAN framework is that it is applicable to various different datasets without additional changes, whilst achieving stable results that are competitive to the state-of-the-art in video prediction. Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "from here to there video inbetweening using direct 3d convolutions"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "phasenet for video frame interpolation"
    - "a two stream variational adversarial network for video generation"
    - "stochastic adversarial video prediction"
    - "style separation and synthesis via generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"
    - "stochastic dynamics for video infilling"

Connected through hyperedge HE-2 (induced by source paper "point to point video generation"):
  with members:
    - "diversity sensitive conditional generative adversarial networks"
    - "phasenet for video frame interpolation"
    - "conditional video generation using action appearance captions"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "self supervised learning of a facial attribute embedding from video"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-3 (induced by source paper "enhancing traffic scene predictions with generative adversarial networks"):
  with members:
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning to forecast and refine residual motion for image to video generation"
    - "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction"
    - "yolov3 an incremental improvement"
    - "disentangling propagation and generation for video prediction"
    - "novel video prediction for large scale scene using optical flow"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "dyan a dynamical atoms based network for video prediction"

Connected through hyperedge HE-4 (induced by source paper "mind2mind transfer learning for gans"):
  with members:
    - "deep learning for classical japanese literature"
    - "pros and cons of gan evaluation measures"
    - "a survey on deep transfer learning"
    - "transferring gans generating images from limited data"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "progressive growing of gans for improved quality stability and variation"
    - "adversarially regularized autoencoders"
    - "improved training of wasserstein gans"

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)