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: "deep learning for environmentally robust speech recognition an overview of recent developments"
  Abstract: Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.

Connected through hyperedge HE-1 (induced by source paper "single channel speech separation with auxiliary speaker embeddings"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "deep attractor network for single microphone speaker separation"
    - "supervised speech separation based on deep learning an overview"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-2 (induced by source paper "audio based automatic mating success prediction of giant pandas"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "attention based models for speech recognition"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dynamic routing between capsules"

Connected through hyperedge HE-4 (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"
    - "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"
    - "speech enhancement in multiple noise conditions using deep neural networks"
    - "a neural network alternative to non negative audio models"

Connected through hyperedge HE-5 (induced by source paper "using recurrences in time and frequency within u net architecture for speech enhancement"):
  with members:
    - "convolutional recurrent neural networks for speech enhancement"
    - "supervised speech separation based on deep learning an overview"
    - "single channel audio source separation using convolutional denoising autoencoders"
    - "a fully convolutional neural network for speech enhancement"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "u net convolutional networks for biomedical image segmentation"
    - "renet a recurrent neural network based alternative to convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "robust speech recognition using generative adversarial networks"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "segan speech enhancement generative adversarial network"
    - "achieving human parity in conversational speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)