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: "interference reduction in music recordings combining kernel additive modelling and non negative matrix factorization"
  Abstract: In live and studio recordings unexpected sound events often lead to interferences in the signal. For non-stationary interferences, sound source separation techniques can be used to reduce the interference level in the recording. In this context, we present a novel approach combining the strengths of two algorithmic families: NMF and KAM. The recent KAM approach applies robust statistics on frames selected by a source-specific kernel to perform source separation. Based on semi-supervised NMF, we extend this approach in two ways. First, we locate the interference in the recording based on detected NMF activity. Second, we improve the kernel-based frame selection by incorporating an NMF-based estimate of the clean music signal. Further, we introduce a temporal context in the kernel, taking some musical structure into account. Our experiments show improved separation quality for our proposed method over a state-of-the-art approach for interference reduction.

Connected through hyperedge HE-1 (induced by source paper "spectral visibility graphs application to similarity of harmonic signals"):
  with members:
    - "does k matter k nn hubness analysis for kernel additive modelling vocal separation"
    - "shift invariant kernel additive modelling for audio source separation"
    - "segregating event streams and noise with a markov renewal process model"

Connected through hyperedge HE-2 (induced by source paper "visibility graphs for robust harmonic similarity measures between audio spectra"):
  with members:
    - "shift invariant kernel additive modelling for audio source separation"
    - "segregating event streams and noise with a markov renewal process model"

Connected through hyperedge HE-3 (induced by source paper "does k matter k nn hubness analysis for kernel additive modelling vocal separation"):
  with members:
    - "shift invariant kernel additive modelling for audio source separation"
    - "on the importance of temporal context in proximity kernels a vocal separation case study"

Connected through hyperedge HE-4 (induced by source paper "shift invariant kernel additive modelling for audio source separation"):
  with members:
    - "on the importance of temporal context in proximity kernels a vocal separation case study"

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)