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: "motion prediction under multimodality with conditional stochastic networks"
  Abstract: Given a visual history, multiple future outcomes for a video scene are equally probable, in other words, the distribution of future outcomes has multiple modes. Multimodality is notoriously hard to handle by standard regressors or classifiers: the former regress to the mean and the latter discretize a continuous high dimensional output space. In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables. Such random variables are sampled from simple Gaussian distributions whose means and variances are parametrized by the output of convolutional encoders over the visual history. We introduce novel convolutional architectures for predicting future body joint trajectories that outperform fully connected alternatives \cite{DBLP:journals/corr/WalkerDGH16}. We introduce stochastic spatial transformers through optical flow warping for predicting future frames, which outperform their deterministic equivalents \cite{DBLP:journals/corr/PatrauceanHC15}. Training stochastic networks involves an intractable marginalization over stochastic variables. We compare various training schemes that handle such marginalization through a) straightforward sampling from the prior, b) conditional variational autoencoders \cite{NIPS2015_5775,DBLP:journals/corr/WalkerDGH16}, and, c) a proposed K-best-sample loss that penalizes the best prediction under a fixed "prediction budget". We show experimental results on object trajectory prediction, human body joint trajectory prediction and video prediction under varying future uncertainty, validating quantitatively and qualitatively our architectural choices and training schemes.

Connected through hyperedge HE-1 (induced by source paper "relational action forecasting"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "recurrent relational networks for complex relational reasoning"
    - "relational recurrent neural networks"
    - "actor centric relation network"
    - "relational inductive biases deep learning and graph networks"
    - "object level visual reasoning in videos"
    - "predicting deeper into the future of semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "point to point video generation"):
  with members:
    - "diversity sensitive conditional generative adversarial networks"
    - "phasenet for video frame interpolation"
    - "conditional video generation using action appearance captions"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "self supervised learning of a facial attribute embedding from video"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"

Connected through hyperedge HE-3 (induced by source paper "compositional video prediction"):
  with members:
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "stochastic video generation with a learned prior"
    - "relational inductive biases deep learning and graph networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "interpretable intuitive physics model"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-4 (induced by source paper "cubic lstms for video prediction"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "recurrent environment simulators"
    - "mocogan decomposing motion and content for video generation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-5 (induced by source paper "accurate and diverse sampling of sequences based on a best of many sample objective"):
  with members:
    - "simplified stochastic feedforward neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "a generative model of people in clothing"
    - "unsupervised learning for physical interaction through video prediction"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "variational inference for monte carlo objectives"

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)