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: "a neural stochastic volatility model"
  Abstract: In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms mainstream methods, e.g., deterministic models such as GARCH and its variants, and stochastic models namely the MCMC-based model \emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol}, on average negative log-likelihood.

Connected through hyperedge HE-1 (induced by source paper "modeling continuous stochastic processes with dynamic normalizing flows"):
  with members:
    - "scalable gradients for stochastic differential equations"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "sequential neural processes"
    - "attentive neural processes"
    - "deepmind control suite"
    - "neural processes"
    - "conditional neural processes"
    - "invertible residual networks"

Connected through hyperedge HE-2 (induced by source paper "benchmarking deep sequential models on volatility predictions for financial time series"):
  with members:
    - "fast slow recurrent neural networks"
    - "recurrent highway networks"
    - "strongly typed recurrent neural networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "pixel recurrent neural networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "deep learning in neural networks"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "variational self attention model for sentence representation"):
  with members:
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "a decomposable attention model for natural language inference"
    - "neural variational inference for text processing"
    - "neural machine translation by jointly learning to align and translate"
    - "distributed representations of words and phrases and their compositionality"

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.LG(Machine Learning)