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: "video prediction with appearance and motion conditions"
  Abstract: Video prediction aims to generate realistic future frames by learning dynamic visual patterns. One fundamental challenge is to deal with future uncertainty: How should a model behave when there are multiple correct, equally probable future? We propose an Appearance-Motion Conditional GAN to address this challenge. We provide appearance and motion information as conditions that specify how the future may look like, reducing the level of uncertainty. Our model consists of a generator, two discriminators taking charge of appearance and motion pathways, and a perceptual ranking module that encourages videos of similar conditions to look similar. To train our model, we develop a novel conditioning scheme that consists of different combinations of appearance and motion conditions. We evaluate our model using facial expression and human action datasets and report favorable results compared to existing 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 "harmonizing maximum likelihood with gans for multimodal conditional generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "video to video synthesis"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-3 (induced by source paper "g 3 an this video does not exist disentangling motion and appearance for video generation"):
  with members:
    - "video generation from single semantic label map"
    - "guided image to image translation with bi directional feature transformation"
    - "pose guided human video generation"
    - "learning to forecast and refine residual motion for image to video generation"
    - "smit stochastic multi label image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "flow grounded spatial temporal video prediction from still images"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "diversity sensitive conditional generative adversarial networks"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "the gan landscape losses architectures regularization and normalization"
    - "stochastic adversarial video prediction"
    - "spectral normalization for generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "self supervised visual planning with temporal skip connections"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "adversarial framework for unsupervised learning of motion dynamics in videos"):
  with members:
    - "video to video synthesis"
    - "tracking emerges by colorizing videos"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "hierarchical video generation from orthogonal information optical flow and texture"
    - "data distillation towards omni supervised learning"

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)