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: "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
  Abstract: Generative models using neural network have opened a door to large-scale studies for various application domains, especially for studies that suffer from lack of real samples to obtain statistically robust inference. Typically, these generative models would train on existing data to learn the underlying distribution of the measurements (e.g., images) in latent spaces conditioned on covariates (e.g., image labels), and generate independent samples that are identically distributed in the latent space. Such models may work for cross-sectional studies, however, they are not suitable to generate data for longitudinal studies that focus on "progressive" behavior in a sequence of data. In practice, this is a quite common case in various neuroimaging studies whose goal is to characterize a trajectory of pathologies of a specific disease even from early stages. This may be too ambitious especially when the sample size is small (e.g., up to a few hundreds). Motivated from the setup above, we seek to develop a conditional generative model for longitudinal data generation by designing an invertable neural network. Inspired by recurrent nature of longitudinal data, we propose a novel neural network that incorporates recurrent subnetwork and context gating to include smooth transition in a sequence of generated data. Our model is validated on a video sequence dataset and a longitudinal AD dataset with various experimental settings for qualitative and quantitative evaluations of the generated samples. The results with the AD dataset captures AD specific group differences with sufficiently generated longitudinal samples that are consistent with existing literature, which implies a great potential to be applicable to other disease studies.

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"
    - "videoflow a flow based generative model for video"
    - "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"
    - "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 "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "a rad approach to deep mixture models"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural voice cloning with a few samples"
    - "efficient neural audio synthesis"

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)