human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "toeplitz inverse covariance based robust speaker clustering for naturalistic audio streams"):
  Abstract: Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The ivectors extracted from different segments of same speaker are correlated. We model this correlation with a Markov Random Field (MRF) network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse Covariance (TIC) matrix to represent the MRF correlation network for each speaker. This approaches captures the sequential structure of i-vectors (or equivalent speaker turns) belonging to same speaker in an audio stream. A variant of standard Expectation Maximization (EM) algorithm is adopted for deriving closed-form solution using dynamic programming (DP) and the alternating direction method of multiplier (ADMM). Our diarization system has four steps: (1) ground-truth segmentation; (2) i-vector extraction; (3) post-processing (mean subtraction, principal component analysis, and length-normalization) ; and (4) proposed speaker clustering. We employ cosine K-means and movMF speaker clustering as baseline approaches. Our evaluation data is derived from: (i) CRSS-PLTL corpus, and (ii) two meetings subset of the AMI corpus. Relative reduction in diarization error rate (DER) for CRSS-PLTL corpus is 43.22% using the proposed advancements as compared to baseline. For AMI meetings IS1000a and IS1003b, relative DER reduction is 29.37% and 9.21%, respectively.

Includes members:
  Member "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams":
    also connected through other hyperedges:
      (none)
  Member "robust feature clustering for unsupervised speech activity detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams")
  Member "using speech technology for quantifying behavioral characteristics in peer led team learning sessions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams")
  Member "toeplitz inverse covariance based clustering of multivariate time series data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "indirect adversarial attacks via poisoning neighbors for graph convolutional networks")
  Member "a robust diarization system for measuring dominance in peer led team learning groups":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams")
  Member "a speaker diarization system for studying peer led team learning groups":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust speaker clustering using mixtures of von mises fisher distributions for naturalistic audio streams")

Question: Please tell me which class the source paper 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)