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 hybrid approach for speech enhancement using mog model and neural network phoneme classifier"
  Abstract: In this paper we present a single-microphone speech enhancement algorithm. A hybrid approach is proposed merging the generative mixture of Gaussians (MoG) model and the discriminative neural network (NN). The proposed algorithm is executed in two phases, the training phase, which does not recur, and the test phase. First, the noise-free speech power spectral density (PSD) is modeled as a MoG, representing the phoneme based diversity in the speech signal. An NN is then trained with phoneme labeled database for phoneme classification with mel-frequency cepstral coefficients (MFCC) as the input features. Given the phoneme classification results, a speech presence probability (SPP) is obtained using both the generative and discriminative models. Soft spectral subtraction is then executed while simultaneously, the noise estimation is updated. The discriminative NN maintain the continuity of the speech and the generative phoneme-based MoG preserves the speech spectral structure. Extensive experimental study using real speech and noise signals is provided. We also compare the proposed algorithm with alternative speech enhancement algorithms. We show that we obtain a significant improvement over previous methods in terms of both speech quality measures and speech recognition results.

Connected through hyperedge HE-1 (induced by source paper "on the importance of super gaussian speech priors for machine learning based speech enhancement"):
  with members:
    - "a state space approach to dynamic nonnegative matrix factorization"
    - "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-2 (induced by source paper "speech enhancement using a deep mixture of experts"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)