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: "online localization and tracking of multiple moving speakers in reverberant environments"
  Abstract: We address the problem of online localization and tracking of multiple moving speakers in reverberant environments. This paper has the following contributions. We use the direct-path relative transfer function (DP-RTF), an interchannel feature that encodes acoustic information robust against reverberation, and we propose an online algorithm well suited for estimating DP-RTFs associated with moving audio sources. Another crucial ingredient of the proposed method is its ability to properly assign DP-RTFs to audio-source directions. Toward this goal, we adopt a maximum-likelihood formulation and we propose to use exponentiated gradient to efficiently update source-direction estimates starting from their currently available values. The problem of multiple speaker tracking is computationally intractable because the number of possible associations between observed source directions and physical speakers grows exponentially with time. We adopt a Bayesian framework and we propose a variational approximation of the posterior filtering distribution associated with multiple speaker tracking, as well as an efficient variational expectation maximization solver. The proposed online localization and tracking method is thoroughly evaluated using two datasets that contain recordings performed in real environments.

Connected through hyperedge HE-1 (induced by source paper "high resolution speaker counting in reverberant rooms using crnn with ambisonics features"):
  with members:
    - "adam a method for stochastic optimization"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-2 (induced by source paper "self supervised moving vehicle tracking with stereo sound"):
  with members:
    - "the sound of motions"
    - "self supervised audio visual co segmentation"
    - "learning to localize sound source in visual scenes"
    - "variational bayesian inference for audio visual tracking of multiple speakers"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-3 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers"):
  with members:
    - "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"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "a cascaded multiple speaker localization and tracking system"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "estimation of the direct path relative transfer function for supervised sound source localization"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-5 (induced by source paper "tracking multiple audio sources with the von mises distribution and variational em"):
  with members:
    - "variational bayesian inference for audio visual tracking of multiple speakers"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"

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.SD(Sound)