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 network of deep neural networks for distant speech recognition"
  Abstract: Despite the remarkable progress recently made in distant speech recognition, state-of-the-art technology still suffers from a lack of robustness, especially when adverse acoustic conditions characterized by non-stationary noises and reverberation are met. A prominent limitation of current systems lies in the lack of matching and communication between the various technologies involved in the distant speech recognition process. The speech enhancement and speech recognition modules are, for instance, often trained independently. Moreover, the speech enhancement normally helps the speech recognizer, but the output of the latter is not commonly used, in turn, to improve the speech enhancement. To address both concerns, we propose a novel architecture based on a network of deep neural networks, where all the components are jointly trained and better cooperate with each other thanks to a full communication scheme between them. Experiments, conducted using different datasets, tasks and acoustic conditions, revealed that the proposed framework can overtake other competitive solutions, including recent joint training approaches.

Connected through hyperedge HE-1 (induced by source paper "unpaired speech enhancement by acoustic and adversarial supervision for speech recognition"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "segan speech enhancement generative adversarial network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "a wavenet for speech denoising"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "algorithm for training neural networks on resistive device arrays"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "dual path networks"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "exploring the limits of language modeling"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-3 (induced by source paper "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "black box adversarial attacks with limited queries and information"
    - "stealing hyperparameters in machine learning"
    - "dolphinattack inaudible voice commands"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "inaudible voice commands"
    - "efficient defenses against adversarial attacks"

Connected through hyperedge HE-4 (induced by source paper "training lstm networks with resistive cross point devices"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "dilated recurrent neural networks"
    - "dual path networks"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "exploring the limits of language modeling"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "distilling knowledge using parallel data for far field speech recognition"):
  with members:
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "the microsoft 2016 conversational speech recognition system"
    - "transferring knowledge from a rnn to a dnn"
    - "recurrent models for auditory attention in multi microphone distance speech recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-6 (induced by source paper "deep learning for environmentally robust speech recognition an overview of recent developments"):
  with members:
    - "generative adversarial networks an overview"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "multichannel end to end speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"

Connected through hyperedge HE-7 (induced by source paper "bridgenets student teacher transfer learning based on recursive neural networks and its application to distant speech recognition"):
  with members:
    - "fitnets hints for thin deep nets"

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.CL(Computation and Language)