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: "accommodating sample size effect on similarity measures in speaker clustering"
  Abstract: We investigate the symmetric Kullback-Leibler (KL2) distance in speaker clustering and its unreported effects for differently-sized feature matrices. Speaker data is represented as Mel Frequency Cepstral Coefficient (MFCC) vectors, and features are compared using the KL2 metric to form clusters of speech segments for each speaker. We make two observations with respect to clustering based on KL2: 1.) The accuracy of clustering is strongly dependent on the absolute lengths of the speech segments and their extracted feature vectors. 2.) The accuracy of the similarity measure strongly degrades with the length of the shorter of the two speech segments. These effects of length can be attributed to the measure of covariance used in KL2. We demonstrate an empirical correction of this sample-size effect that increases clustering accuracy. We draw parallels to two Vector Quantization-based (VQ) similarity measures, one which exhibits an equivalent effect of sample size, and the second being less influenced by it.

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)