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: "transformation based models of video sequences"
  Abstract: In this work we propose a simple unsupervised approach for next frame prediction in video. Instead of directly predicting the pixels in a frame given past frames, we predict the transformations needed for generating the next frame in a sequence, given the transformations of the past frames. This leads to sharper results, while using a smaller prediction model.#N##N#In order to enable a fair comparison between different video frame prediction models, we also propose a new evaluation protocol. We use generated frames as input to a classifier trained with ground truth sequences. This criterion guarantees that models scoring high are those producing sequences which preserve discrim- inative features, as opposed to merely penalizing any deviation, plausible or not, from the ground truth. Our proposed approach compares favourably against more sophisticated ones on the UCF-101 data set, while also being more efficient in terms of the number of parameters and computational cost.

Connected through hyperedge HE-1 (induced by source paper "first order motion model for image animation"):
  with members:
    - "textured neural avatars"
    - "few shot adversarial learning of realistic neural talking head models"
    - "dwnet dense warp based network for pose guided human video generation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesture to gesture translation in the wild via category independent conditional maps"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "learning to forecast and refine residual motion for image to video generation"

Connected through hyperedge HE-2 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

Connected through hyperedge HE-3 (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"
    - "towards accurate generative models of video a new metric challenges"

Connected through hyperedge HE-4 (induced by source paper "brain inspired inference on missing video sequence"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "energy based generative adversarial network"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "video pixel networks"
    - "unsupervised learning of video representations using lstms"
    - "action conditional video prediction using deep networks in atari games"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-5 (induced by source paper "animating arbitrary objects via deep motion transfer"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "recycle gan unsupervised video retargeting"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-6 (induced by source paper "learning to decompose and disentangle representations for video prediction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "visual robot task planning"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "folded recurrent neural networks for future video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "neural expectation maximization"

Connected through hyperedge HE-7 (induced by source paper "stochastic adversarial video prediction"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "recurrent environment simulators"

Connected through hyperedge HE-8 (induced by source paper "diy human action data set generation"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "progressive growing of gans for improved quality stability and variation"
    - "unsupervised image to image translation networks"
    - "the kinetics human action video dataset"
    - "mocogan decomposing motion and content for video generation"
    - "the pose knows video forecasting by generating pose futures"
    - "unsupervised learning for physical interaction through video prediction"

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.LG(Machine Learning)