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 "meta particle flow for sequential bayesian inference"):
  Abstract: We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists and its neural parameterization can be trained in a meta-learning framework, allowing this operator to reason about the effect of an individual observation on the posterior, and thus generalize across different priors, observations and to online Bayesian inference. We demonstrated the generalization ability of our particle flow Bayes operator in several canonical and high dimensional examples.

Includes members:
  Member "monge amp ere flow for generative modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "deep sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction")
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic latent residual video prediction")
  Member "ffjord free form continuous dynamics for scalable reversible generative models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "filtering variational objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "a geometric view of optimal transportation and generative model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation")
  Member "learning combinatorial optimization algorithms over graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lossless compression of deep neural networks")

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)