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: "a speaker diarization system for studying peer led team learning groups"
  Abstract: Peer-led team learning (PLTL) is a model for teaching STEM courses where small student groups meet periodically to collaboratively discuss coursework. Automatic analysis of PLTL sessions would help education researchers to get insight into how learning outcomes are impacted by individual participation, group behavior, team dynamics, etc.. Towards this, speech and language technology can help, and speaker diarization technology will lay the foundation for analysis. In this study, a new corpus is established called CRSS-PLTL, that contains speech data from 5 PLTL teams over a semester (10 sessions per team with 5-to-8 participants in each team). In CRSS-PLTL, every participant wears a LENA device (portable audio recorder) that provides multiple audio recordings of the event. Our proposed solution is unsupervised and contains a new online speaker change detection algorithm, termed G 3 algorithm in conjunction with Hausdorff-distance based clustering to provide improved detection accuracy. Additionally, we also exploit cross channel information to refine our diarization hypothesis. The proposed system provides good improvements in diarization error rate (DER) over the baseline LIUM system. We also present higher level analysis such as the number of conversational turns taken in a session, and speaking-time duration (participation) for each speaker.

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"
    - "robust feature clustering for unsupervised speech activity detection"
    - "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"

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:
    - "robust feature clustering for unsupervised speech activity detection"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "using speech technology for quantifying behavioral characteristics in peer led team learning sessions"):
  with members:
    - "a robust diarization system for measuring dominance in peer led team learning groups"

Connected through hyperedge HE-4 (induced by source paper "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"):
  with members:
    - "a robust diarization system for measuring dominance in peer led team learning groups"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings"
    - "photoplethysmography based heart rate monitoring in physical activities via joint sparse spectrum reconstruction"
    - "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise"
    - "fog data enhancing telehealth big data through fog computing"

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)