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: "co localization of audio sources in images using binaural features and locally linear regression"
  Abstract: This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.

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 "audio visual speaker diarization based on spatiotemporal bayesian fusion"):
  with members:
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"

Connected through hyperedge HE-3 (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"
    - "spatial source subtraction based on incomplete measurements of relative transfer function"
    - "semi supervised sound source localization based on manifold regularization"
    - "high dimensional regression with gaussian mixtures and partially latent response variables"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"):
  with members:
    - "semi supervised source localization on multiple manifolds with distributed microphones"
    - "rectified binaural ratio a complex t distributed feature for robust sound localization"
    - "estimation of the direct path relative transfer function for supervised sound source localization"
    - "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"

Connected through hyperedge HE-5 (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"
    - "high dimensional regression with gaussian mixtures and partially latent response variables"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"

Connected through hyperedge HE-6 (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"
    - "semi supervised sound source localization based on manifold regularization"
    - "high dimensional regression with gaussian mixtures and partially latent response variables"

Connected through hyperedge HE-7 (induced by source paper "estimation of the direct path relative transfer function for supervised sound source localization"):
  with members:
    - "binaural coherent to diffuse ratio estimation for dereverberation using an itd model"
    - "coherent to diffuse power ratio estimation for dereverberation"
    - "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.SD(Sound)