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: "tganv2 efficient training of large models for video generation with multiple subsampling layers"
  Abstract: In this paper, we propose a novel method to efficiently train a Generative Adversarial Network (GAN) on high dimensional samples. The key idea is to introduce a differentiable subsampling layer which appropriately reduces the dimensionality of intermediate feature maps in the generator during training. In general, generators require large memory and computational costs in the latter stages of the network as the feature maps become larger, though the latter stages have relatively fewer parameters than the earlier stages. It makes training large models for video generation difficult due to the limited computational resource. We solve this problem by introducing a method that gradually reduces the dimensionality of feature maps in the generator with multiple subsampling layers. We also propose a network (Temporal GAN v2) with such layers and perform video generation experiments. As a consequence, our model trained on the UCF101 dataset at $192 \times 192$ pixels achieves an Inception Score (IS) of 24.34, which shows a significant improvement over the previous state-of-the-art score of 14.56.

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"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "high fidelity speech synthesis with adversarial networks"):
  with members:
    - "large scale adversarial representation learning"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "expediting tts synthesis with adversarial vocoding"
    - "waveglow a flow based generative network for speech synthesis"
    - "efficient neural audio synthesis"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "lower dimensional kernels for video discriminators"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "the mechanics of n player differentiable games"
    - "pros and cons of gan evaluation measures"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "towards high resolution video generation with progressive growing of sliced wasserstein gans"
    - "which training methods for gans do actually converge"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-5 (induced by source paper "self paced video data augmentation with dynamic images generated by generative adversarial networks"):
  with members:
    - "few shot video classification via temporal alignment"
    - "recycle gan unsupervised video retargeting"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "spectral normalization for generative adversarial networks"
    - "towards high resolution video generation with progressive growing of sliced wasserstein gans"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hidden two stream convolutional networks for action recognition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

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)