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 sound source localization based on manifold regularization"
  Abstract: Conventional speaker localization algorithms, based merely on the received microphone signals, are often sensitive to adverse conditions, such as: high reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in meeting rooms or cars, it can be assumed that the source position is confined to a predefined area, and the acoustic parameters of the environment are approximately fixed. Such scenarios give rise to the assumption that the acoustic samples from the region of interest have a distinct geometrical structure. In this paper, we show that the high dimensional acoustic samples indeed lie on a low dimensional manifold and can be embedded into a low dimensional space. Motivated by this result, we propose a semi-supervised source localization algorithm which recovers the inverse mapping between the acoustic samples and their corresponding locations. The idea is to use an optimization framework based on manifold regularization, that involves smoothness constraints of possible solutions with respect to the manifold. The proposed algorithm, termed Manifold Regularization for Localization (MRL), is implemented in an adaptive manner. The initialization is conducted with only few labelled samples attached with their respective source locations, and then the system is gradually adapted as new unlabelled samples (with unknown source locations) are received. Experimental results show superior localization performance when compared with a recently presented algorithm based on a manifold learning approach and with the generalized cross-correlation (GCC) algorithm as a baseline.

Connected through hyperedge HE-1 (induced by source paper "indoor sound source localization with probabilistic neural network"):
  with members:
    - "a geometric approach to sound source localization from time delay estimates"

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"
    - "semi supervised source localization on multiple manifolds with distributed microphones"
    - "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"
    - "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:
    - "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"
    - "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"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"

Connected through hyperedge HE-4 (induced by source paper "semi supervised source localization on multiple manifolds with distributed microphones"):
  with members:
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "towards a generalization of relative transfer functions to more than one source"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "robust sound source localization using a microphone array on a mobile robot"

Connected through hyperedge HE-5 (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"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "high dimensional regression with gaussian mixtures and partially latent response variables"

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)