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: "videoflow a flow based generative model for video"
  Abstract: Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. In particular, learning predictive models of videos offers an especially appealing mechanism to enable a rich understanding of the physical world: videos of real-world interactions are plentiful and readily available, and a model that can predict future video frames can not only capture useful representations of the world, but can be useful in its own right, for problems such as model-based robotic control. However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possible futures. Although a number of recent works have studied probabilistic models that can represent uncertain futures, such models are either extremely expensive computationally (as in the case of pixel-level autoregressive models), or do not directly optimize the likelihood of the data. In this work, we propose a model for video prediction based on normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions. To our knowledge, our work is the first to propose multi-frame video prediction with normalizing flows. We describe an approach for modeling the latent space dynamics, and demonstrate that flow-based generative models offer a viable and competitive approach to generative modeling of video.

Connected through hyperedge HE-1 (induced by source paper "multi variate probabilistic time series forecasting via conditioned normalizing flows"):
  with members:
    - "high dimensional multivariate forecasting with low rank gaussian copula processes"
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "relational inductive biases deep learning and graph networks"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"

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"
    - "generating long sequences with sparse transformers"
    - "scaling autoregressive video models"
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "pose guided human video generation"

Connected through hyperedge HE-3 (induced by source paper "pointflow 3d point cloud generation with continuous normalizing flows"):
  with members:
    - "pointwise an unsupervised point wise feature learning network"
    - "waveglow a flow based generative network for speech synthesis"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep sets"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "point cloud gan"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "c flow conditional generative flow models for images and 3d point clouds"):
  with members:
    - "revealing scenes by inverting structure from motion reconstructions"
    - "conditional adversarial generative flow for controllable image synthesis"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "neural point based graphics"
    - "fast video object segmentation with spatio temporal gans"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"

Connected through hyperedge HE-5 (induced by source paper "generative models for effective ml on private decentralized datasets"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "taxonomy of real faults in deep learning systems"
    - "reconstruction and membership inference attacks against generative models"
    - "towards federated learning at scale system design"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "evaluating differentially private machine learning in practice"
    - "detecting bias with generative counterfactual face attribute augmentation"
    - "auditing data provenance in text generation models"

Connected through hyperedge HE-6 (induced by source paper "learning to predict without looking ahead world models without forward prediction"):
  with members:
    - "weight agnostic neural networks"
    - "reinforcement learning for improving agent design"
    - "model based reinforcement learning for atari"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"
    - "woulda coulda shoulda counterfactually guided policy search"

Connected through hyperedge HE-7 (induced by source paper "guided image generation with conditional invertible neural networks"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "training generative reversible networks"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"

Connected through hyperedge HE-8 (induced by source paper "conditional flow variational autoencoders for structured sequence prediction"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "scene induced multi modal trajectory forecasting via planning"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "preventing posterior collapse with delta vaes"
    - "structured output learning with conditional generative flows"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"

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)