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: "variational inference in non negative factorial hidden markov models for efficient audio source separation"
  Abstract: The past decade has seen substantial work on the use of non-negative matrix factorization and its probabilistic counterparts for audio source separation. Although able to capture audio spectral structure well, these models neglect the non-stationarity and temporal dynamics that are important properties of audio. The recently proposed non-negative factorial hidden Markov model (N-FHMM) introduces a temporal dimension and improves source separation performance. However, the factorial nature of this model makes the complexity of inference exponential in the number of sound sources. Here, we present a Bayesian variant of the N-FHMM suited to an efficient variational inference algorithm, whose complexity is linear in the number of sound sources. Our algorithm performs comparably to exact inference in the original N-FHMM but is significantly faster. In typical configurations of the N-FHMM, our method achieves around a 30x increase in speed.

Connected through hyperedge HE-1 (induced by source paper "an augmented lagrangian method for piano transcription using equal loudness thresholding and lstm based decoding"):
  with members:
    - "on the potential of simple framewise approaches to piano transcription"
    - "piano transcription in the studio using an extensible alternating directions framework"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "practical recommendations for gradient based training of deep architectures"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-2 (induced by source paper "a state space approach to dynamic nonnegative matrix factorization"):
  with members:
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"
    - "nonnegative hmm for babble noise derived from speech hmm application to speech enhancement"

Connected through hyperedge HE-3 (induced by source paper "deep unfolding model based inspiration of novel deep architectures"):
  with members:
    - "negative tree reweighted belief propagation"

Connected through hyperedge HE-4 (induced by source paper "improved multiple birdsong tracking with distribution derivative method and markov renewal process clustering"):
  with members:
    - "segregating event streams and noise with a markov renewal process model"

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.LG(Machine Learning)