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 "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"):
  Abstract: This paper addresses the problem of multiple-speaker localization in noisy and reverberant environments, using binaural recordings of an acoustic scene. A complex-valued Gaussian mixture model (CGMM) is adopted, whose components correspond to all the possible candidate source locations defined on a grid. After optimizing the CGMM-based objective function, given an observed set of complex-valued binaural features, both the number of sources and their locations are estimated by selecting the CGMM components with the largest weights. An entropy-based penalty term is added to the likelihood to impose sparsity over the set of CGMM component weights. This favors a small number of detected speakers with respect to the large number of initial candidate source locations. In addition, the direct-path relative transfer function (DP-RTF) is used to build robust binaural features. The DP-RTF, recently proposed for single-source localization, encodes interchannel information corresponding to the direct path of sound propagation and is thus robust to reverberations. In this paper, we extend the DP-RTF estimation to the case of multiple sources. In the short-time Fourier transform domain, a consistency test is proposed to check whether a set of consecutive frames is associated with the same source or not. Reliable DP-RTF features are selected from the frames that pass the consistency test to be used for source localization. Experiments carried out using both simulation data and real data recorded with a robotic head confirm the efficiency of the proposed multisource localization method.

Includes members:
  Member "semi supervised source localization on multiple manifolds with distributed microphones":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield")
  Member "rectified binaural ratio a complex t distributed feature for robust sound localization":
    also connected through other hyperedges:
      (none)
  Member "estimation of the direct path relative transfer function for supervised sound source localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a cascaded multiple speaker localization and tracking system")
  Member "co localization of audio sources in images using binaural features and locally linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers")
  Member "towards a generalization of relative transfer functions to more than one source":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised source localization on multiple manifolds with distributed microphones")
  Member "semi supervised sound source localization based on manifold regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "indoor sound source localization with probabilistic neural network")
  Member "acoustic space learning for sound source separation and localization on binaural manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling the comb filter effect and interaural coherence for binaural source separation")

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.SD(Sound)