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: "fastfca a joint diagonalization based fast algorithm for audio source separation using a full rank spatial covariance model"
  Abstract: A source separation method using a full-rank spatial covariance model has been proposed by Duong et al. ["Under-determined Reverberant Audio Source Separation Using a Full-rank Spatial Covariance Model," IEEE Trans. ASLP, vol. 18, no. 7, pp. 1830-1840, Sep. 2010], which is referred to as full-rank spatial covariance analysis (FCA) in this paper. Here we propose a fast algorithm for estimating the model parameters of the FCA, which is named Fast-FCA, and applicable to the two-source case. Though quite effective in source separation, the conventional FCA has a major drawback of expensive computation. Indeed, the conventional algorithm for estimating the model parameters of the FCA requires frame-wise matrix inversion and matrix multiplication. Therefore, the conventional FCA may be infeasible in applications with restricted computational resources. In contrast, the proposed FastFCA bypasses matrix inversion and matrix multiplication owing to joint diagonalization based on the generalized eigenvalue problem. Furthermore, the FastFCA is strictly equivalent to the conventional algorithm. An experiment has shown that the FastFCA was over 250 times faster than the conventional algorithm with virtually the same source separation performance.

Connected through hyperedge HE-1 (induced by source paper "fast multichannel source separation based on jointly diagonalizable spatial covariance matrices"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "fastfca as joint diagonalization based acceleration of full rank spatial covariance analysis for separating any number of sources"
    - "a variance modeling framework based on variational autoencoders for speech enhancement"

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)