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: "towards accurate generative models of video a new metric challenges"
  Abstract: Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider videos. Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects. While recent attempts at formulating generative models of video have had some success, current progress is hampered by (1) the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples, and (2) the wide gap between purely synthetic video data sets and challenging real-world data sets in terms of complexity. To this extent we propose Frechet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video. We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos, and provide initial benchmark results on SCV.

Connected through hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "large scale adversarial representation learning"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarial video generation on complex datasets"
    - "scaling autoregressive video models"
    - "object oriented dynamics predictor"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction"):
  with members:
    - "videoflow a conditional flow based model for stochastic video generation"
    - "improved conditional vrnns for video prediction"
    - "cubic lstms for video prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "scaling autoregressive video models"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-3 (induced by source paper "videoflow a conditional flow based model for stochastic video generation"):
  with members:
    - "waveglow a flow based generative network for speech synthesis"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "fast generation for convolutional autoregressive models"
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-4 (induced by source paper "adversarial video generation on complex datasets"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "dance dance generation motion transfer for internet videos"
    - "generating diverse high fidelity images with vq vae 2"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "scaling autoregressive video models"
    - "cgans with projection discriminator"
    - "video to video synthesis"

Connected through hyperedge HE-5 (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"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "style separation and synthesis via generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "stochastic dynamics for video infilling"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of object structure and dynamics from videos"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "unsupervised learning of object keypoints for perception and control"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "unsupervised discovery of parts structure and dynamics"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-7 (induced by source paper "scaling autoregressive video models"):
  with members:
    - "model based reinforcement learning for atari"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "a short note about kinetics 600"
    - "blockwise parallel decoding for deep autoregressive models"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-8 (induced by source paper "s flow gan"):
  with members:
    - "quantifying the reality gap in robotic manipulation tasks"
    - "the perception distortion tradeoff"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "large receptive field networks for high scale image super resolution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "understanding the effective receptive field in deep convolutional neural networks"

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)