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: "high dimensional regression with gaussian mixtures and partially latent response variables"
  Abstract: The problem of approximating high-dimensional data with a low-dimensional representation is addressed. The article makes the following contributions. An inverse regression framework is proposed, which exchanges the roles of input and response, such that the low-dimensional variable becomes the regressor, and which is tractable. A mixture of locally-linear probabilistic mapping model is introduced, that starts with estimating the parameters of the inverse regression, and follows with inferring closed-form solutions for the forward parameters of the high-dimensional regression problem of interest. Moreover, a partially-latent paradigm is introduced, such that the vector-valued response variable is composed of both observed and latent entries, thus being able to deal with data contaminated by experimental artifacts that cannot be explained with noise models. The proposed probabilistic formulation could be viewed as a latent-variable augmentation of regression. Expectation-maximization (EM) procedures are introduced, based on a data augmentation strategy which facilitates the maximum-likelihood search over the model parameters. Two augmentation schemes are proposed and the associated EM inference procedures are described in detail; they may well be viewed as generalizations of a number of EM regression, dimension reduction, and factor analysis algorithms. The proposed framework is validated with both synthetic and real data. Experimental evidence is provided that the method outperforms several existing regression techniques.

Connected through hyperedge HE-1 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "tracking multiple audio sources with the von mises distribution and variational em"
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "mot16 a benchmark for multi object tracking"
    - "person re identification in the wild"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"

Connected through hyperedge HE-2 (induced by source paper "relative transfer function inverse regression from low dimensional manifold"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "semi supervised source localization on multiple manifolds with distributed microphones"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "spatial source subtraction based on incomplete measurements of relative transfer function"
    - "semi supervised sound source localization based on manifold regularization"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "vast the virtual acoustic space traveler dataset"):
  with members:
    - "hearing in a shoe box binaural source position and wall absorption estimation using virtually supervised learning"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"

Connected through hyperedge HE-4 (induced by source paper "hearing in a shoe box binaural source position and wall absorption estimation using virtually supervised learning"):
  with members:
    - "vast the virtual acoustic space traveler dataset"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "semi supervised sound source localization based on manifold regularization"

Connected through hyperedge HE-5 (induced by source paper "co localization of audio sources in images using binaural features and locally linear regression"):
  with members:
    - "acoustic space learning for sound source separation and localization on binaural manifolds"

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)