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 "neuro serket development of integrative cognitive system through the composition of deep probabilistic generative models"):
  Abstract: This paper describes a framework for the development of an integrative cognitive system based on probabilistic generative models (PGMs) called Neuro-SERKET. Neuro-SERKET is an extension of SERKET, which can compose elemental PGMs developed in a distributed manner and provide a scheme that allows the composed PGMs to learn throughout the system in an unsupervised way. In addition to the head-to-tail connection supported by SERKET, Neuro-SERKET supports tail-to-tail and head-to-head connections, as well as neural network-based modules, i.e., deep generative models. As an example of a Neuro-SERKET application, an integrative model was developed by composing a variational autoencoder (VAE), a Gaussian mixture model (GMM), latent Dirichlet allocation (LDA), and automatic speech recognition (ASR). The model is called VAE+GMM+LDA+ASR. The performance of VAE+GMM+LDA+ASR and the validity of Neuro-SERKET were demonstrated through a multimodal categorization task using image data and a speech signal of numerical digits.

Includes members:
  Member "cross modal variational auto encoder with distributed latent spaces and associators":
    also connected through other hyperedges:
      (none)
  Member "multimodal generative models for scalable weakly supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational item response theory fast accurate and expressive")
  Member "deep unsupervised clustering with gaussian mixture variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "online spatial concept and lexical acquisition with simultaneous localization and mapping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatial concept based navigation with human speech instructions via probabilistic inference on bayesian generative model")
  Member "serket an architecture for connecting stochastic models to realize a large scale cognitive model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symbol emergence as an interpersonal multimodal categorization")
  Member "spatial concept acquisition for a mobile robot that integrates self localization and unsupervised word discovery from spoken sentences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatial concept based navigation with human speech instructions via probabilistic inference on bayesian generative model")
  Member "nonparametric bayesian double articulation analyzer for direct language acquisition from continuous speech signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "symbol emergence in cognitive developmental systems a survey")
  Member "variational deep embedding an unsupervised and generative approach to clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")

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)