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: "robust feature clustering for unsupervised speech activity detection"
  Abstract: In certain applications such as zero-resource speech processing or very-low resource speech-language systems, it might not be feasible to collect speech activity detection (SAD) annotations. However, the state-of-the-art supervised SAD techniques based on neural networks or other machine learning methods require annotated training data matched to the target domain. This paper establish a clustering approach for fully unsupervised SAD useful for cases where SAD annotations are not available. The proposed approach leverages Hartigan dip test in a recursive strategy for segmenting the feature space into prominent modes. Statistical dip is invariant to distortions that lends robustness to the proposed method. We evaluate the method on NIST OpenSAD 2015 and NIST OpenSAT 2017 public safety communications data. The results showed the superiority of proposed approach over the two-component GMM baseline. Index Terms: Clustering, Hartigan dip test, NIST OpenSAD, NIST OpenSAT, speech activity detection, zero-resource speech processing, unsupervised learning.

Connected through hyperedge HE-1 (induced by source paper "toeplitz inverse covariance based robust speaker clustering for naturalistic audio streams"):
  with members:
    - "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams"
    - "using speech technology for quantifying behavioral characteristics in peer led team learning sessions"
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "a robust diarization system for measuring dominance in peer led team learning groups"
    - "a speaker diarization system for studying peer led team learning groups"

Connected through hyperedge HE-2 (induced by source paper "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams"):
  with members:
    - "using speech technology for quantifying behavioral characteristics in peer led team learning sessions"
    - "a robust diarization system for measuring dominance in peer led team learning groups"
    - "a speaker diarization system for studying peer led team learning groups"

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)