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: "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.

Connected through hyperedge HE-1 (induced by source paper "sinusoidal wave generating network based on adversarial learning and its application synthesizing frog sounds for data augmentation"):
  with members:
    - "joint alignment of multiple point sets with batch and incremental expectation maximization"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "generative cooperative net for image generation and data augmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved training of wasserstein gans"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-2 (induced by source paper "neural network based reinforcement learning for audio visual gaze control in human robot interaction"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "online localization and tracking of multiple moving speakers in reverberant environments"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-4 (induced by source paper "expectation maximization for speech source separation using convolutive transfer function"):
  with members:
    - "multichannel speech separation and enhancement using the convolutive transfer function"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "tracking multiple audio sources with the von mises distribution and variational em"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "variational bayesian inference for audio visual tracking of multiple speakers"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"

Connected through hyperedge HE-7 (induced by source paper "tracking gaze and visual focus of attention of people involved in social interaction"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "eye tracking for everyone"

Connected through hyperedge HE-8 (induced by source paper "semi supervised source localization on multiple manifolds with distributed microphones"):
  with members:
    - "towards a generalization of relative transfer functions to more than one source"
    - "semi supervised sound source localization based on manifold regularization"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "robust sound source localization using a microphone array on a mobile robot"

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)