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: "multichannel end to end speech recognition"
  Abstract: The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end training of the acoustic and language modeling components. In this paper we extend the end-to-end framework to encompass microphone array signal processing for noise suppression and speech enhancement within the acoustic encoding network. This allows the beamforming components to be optimized jointly within the recognition architecture to improve the end-to-end speech recognition objective. Experiments on the noisy speech benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system outperformed the attention-based baseline with input from a conventional adaptive beamformer.

Connected through hyperedge HE-1 (induced by source paper "fully learnable front end for multi channel acoustic modeling using semi supervised learning"):
  with members:
    - "lessons from building acoustic models with a million hours of speech"
    - "large scale domain adaptation via teacher student learning"

Connected through hyperedge HE-2 (induced by source paper "robust multi channel speech recognition using frequency aligned network"):
  with members:
    - "recurrent models for auditory attention in multi microphone distance speech recognition"

Connected through hyperedge HE-3 (induced by source paper "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral feature mapping with mimic loss for robust speech recognition"
    - "spectral normalization for generative adversarial networks"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"

Connected through hyperedge HE-4 (induced by source paper "espnet end to end speech processing toolkit"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "regularizing neural networks by penalizing confident output distributions"
    - "very deep convolutional networks for end to end speech recognition"

Connected through hyperedge HE-5 (induced by source paper "stream attention based multi array end to end speech recognition"):
  with members:
    - "end to end speech recognition with word based rnn language models"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "multi encoder multi resolution framework for end to end speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

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"
    - "a network of deep neural networks for distant speech recognition"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "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 "attention based end to end speech recognition on voice search"):
  with members:
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the microsoft 2016 conversational speech recognition system"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "deep learning in neural networks"

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)