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: "semi supervised source localization on multiple manifolds with distributed microphones"
  Abstract: The problem of source localization with ad hoc microphone networks in noisy and reverberant enclosures, given a training set of prerecorded measurements, is addressed in this paper. The training set is assumed to consist of a limited number of labelled measurements, attached with corresponding positions, and a larger amount of unlabelled measurements from unknown locations. However, microphone calibration is not required. We use a Bayesian inference approach for estimating a function that maps measurement-based feature vectors to the corresponding positions. The central issue is how to combine the information provided by the different microphones in a unified statistical framework. To address this challenge, we model this function using a Gaussian process with a covariance function that encapsulates both the connections between pairs of microphones and the relations among the samples in the training set. The parameters of the process are estimated by optimizing a maximum likelihood (ML) criterion. In addition, a recursive adaptation mechanism is derived where the new streaming measurements are used to update the model. Performance is demonstrated for 2-D localization of both simulated data and real-life recordings in a variety of reverberation and noise levels.

Connected through hyperedge HE-1 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "indoor sound source localization with probabilistic neural network"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "sound source localization in a multipath environment using convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "a volumetric srp with refinement step for sound source localization"
    - "audio surveillance a systematic review"

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"
    - "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"
    - "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-3 (induced by source paper "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"):
  with members:
    - "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"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "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"

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)