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: "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"
  Abstract: This paper presents two single-channel speech dereverberation methods to enhance the quality of speech signals that have been recorded in an enclosed space. For both methods, the room acoustics are modeled using a non-negative approximation of the convolutive transfer function (N-CTF), and to additionally exploit the spectral properties of the speech signal, such as the low-rank nature of the speech spectrogram, the speech spectrogram is modeled using non-negative matrix factorization (NMF). Two methods are described to combine the N-CTF and NMF models. In the first method, referred to as the integrated method, a cost function is constructed by directly integrating the speech NMF model into the N-CTF model, while in the second method, referred to as the weighted method, the N-CTF and NMF based cost functions are weighted and summed. Efficient update rules are derived to solve both optimization problems. In addition, an extension of the integrated method is presented, which exploits the temporal dependencies of the speech signal. Several experiments are performed on reverberant speech signals with and without background noise, where the integrated method yields a considerably higher speech quality than the baseline N-CTF method and a state-of-the-art spectral enhancement method. Moreover, the experimental results indicate that the weighted method can even lead to a better performance in terms of instrumental quality measures, but that the optimal weighting parameter depends on the room acoustics and the utilized NMF model. Modeling the temporal dependencies in the integrated method was found to be useful only for highly reverberant conditions.

Connected through hyperedge HE-1 (induced by source paper "investigating generative adversarial networks based speech dereverberation for robust speech recognition"):
  with members:
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "the microsoft 2017 conversational speech recognition system"
    - "segan speech enhancement generative adversarial network"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "language modeling with highway lstm"
    - "a fully convolutional neural network for speech enhancement"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "on single channel speech enhancement and on non linear modulation domain kalman filtering"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "modulation domain kalman filtering for monaural blind speech denoising and dereverberation"
    - "on the importance of super gaussian speech priors for machine learning based speech enhancement"
    - "model based speech enhancement in the modulation domain"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "speech enhancement in multiple noise conditions using deep neural networks"
    - "a neural network alternative to non negative audio models"

Connected through hyperedge HE-3 (induced by source paper "multichannel online dereverberation based on spectral magnitude inverse filtering"):
  with members:
    - "multichannel speech separation and enhancement using the convolutive transfer function"
    - "coherent to diffuse power ratio estimation for dereverberation"

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)