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: "temporal generative adversarial nets with singular value clipping"
  Abstract: In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of generating videos. Unlike existing Generative Adversarial Nets (GAN)-based methods that generate videos with a single generator consisting of 3D deconvolutional layers, our model exploits two different types of generators: a temporal generator and an image generator. The temporal generator takes a single latent variable as input and outputs a set of latent variables, each of which corresponds to an image frame in a video. The image generator transforms a set of such latent variables into a video. To deal with instability in training of GAN with such advanced networks, we adopt a recently proposed model, Wasserstein GAN, and propose a novel method to train it stably in an end-to-end manner. The experimental results demonstrate the effectiveness of our methods.

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 "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-3 (induced by source paper "twostreamvan improving motion modeling in video generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-4 (induced by source paper "adacof adaptive collaboration of flows for video frame interpolation"):
  with members:
    - "depth aware video frame interpolation"
    - "phasenet for video frame interpolation"
    - "the perception distortion tradeoff"
    - "memc net motion estimation and motion compensation driven neural network for video interpolation and enhancement"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unsupervised learning of disentangled representations from video"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-5 (induced by source paper "synthesizing human like sketches from natural images using a conditional convolutional decoder"):
  with members:
    - "learning deep sketch abstraction"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "unsupervised learning of depth and ego motion from video"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-6 (induced by source paper "few shot video to video synthesis"):
  with members:
    - "video generation from single semantic label map"
    - "pixel adaptive convolutional neural networks"
    - "dance dance generation motion transfer for internet videos"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "vid2game controllable characters extracted from real world videos"
    - "cgans with projection discriminator"
    - "video to video synthesis"

Connected through hyperedge HE-7 (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-8 (induced by source paper "zero shot generation of human object interaction videos"):
  with members:
    - "video relationship reasoning using gated spatio temporal energy graph"
    - "cgans with projection discriminator"
    - "recycle gan unsupervised video retargeting"
    - "spectral normalization for generative adversarial networks"
    - "probabilistic video generation using holistic attribute control"
    - "large scale gan training for high fidelity natural image synthesis"
    - "videos as space time region graphs"
    - "image generation from scene graphs"

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)