human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "modeling continuous stochastic processes with dynamic normalizing flows"):
  Abstract: Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this work, we propose a novel type of normalizing flow driven by a differential deformation of the continuous-time Wiener process. As a result, we obtain a rich time series model whose observable process inherits many of the appealing properties of its base process, such as efficient computation of likelihoods and marginals. Furthermore, our continuous treatment provides a natural framework for irregular time series with an independent arrival process, including straightforward interpolation. We illustrate the desirable properties of the proposed model on popular stochastic processes and demonstrate its superior flexibility to variational RNN and latent ODE baselines in a series of experiments on synthetic and real-world data.

Includes members:
  Member "scalable gradients for stochastic differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stable neural flows")
  Member "lagging inference networks and posterior collapse in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "sequential neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recurrent attentive neural process for sequential data")
  Member "attentive neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local nonparametric meta learning")
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modelling latent skills for multitask language generation")
  Member "conditional neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local nonparametric meta learning")
  Member "invertible residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on feature normalization and data augmentation")

Question: Please tell me which class the source paper 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)