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: "invariant representations for noisy speech recognition"
  Abstract: Modern automatic speech recognition (ASR) systems need to be robust under acoustic variability arising from environmental, speaker, channel, and recording conditions. Ensuring such robustness to variability is a challenge in modern day neural network-based ASR systems, especially when all types of variability are not seen during training. We attempt to address this problem by encouraging the neural network acoustic model to learn invariant feature representations. We use ideas from recent research on image generation using Generative Adversarial Networks and domain adaptation ideas extending adversarial gradient-based training. A recent work from Ganin et al. proposes to use adversarial training for image domain adaptation by using an intermediate representation from the main target classification network to deteriorate the domain classifier performance through a separate neural network. Our work focuses on investigating neural architectures which produce representations invariant to noise conditions for ASR. We evaluate the proposed architecture on the Aurora-4 task, a popular benchmark for noise robust ASR. We show that our method generalizes better than the standard multi-condition training especially when only a few noise categories are seen during training.

Connected through hyperedge HE-1 (induced by source paper "attentive adversarial learning for domain invariant training"):
  with members:
    - "conditional teacher student learning"
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "domain adversarial training for accented speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "speaker adaptation for attention based end to end speech recognition"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "advancing acoustic to word ctc model"
    - "developing far field speaker system via teacher student learning"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "adversarial target invariant representation learning for domain generalization"):
  with members:
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "on learning invariant representation for domain adaptation"
    - "algorithms and theory for multiple source adaptation"
    - "stabilizing gan training with multiple random projections"
    - "domain generalization with domain specific aggregation modules"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "unified deep supervised domain adaptation and generalization"

Connected through hyperedge HE-4 (induced by source paper "adversarial speaker verification"):
  with members:
    - "adversarial speaker adaptation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "segan speech enhancement generative adversarial network"
    - "deep speaker an end to end neural speaker embedding system"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "end to end attention based text dependent speaker verification"

Connected through hyperedge HE-5 (induced by source paper "niesr nuisance invariant end to end speech recognition"):
  with members:
    - "unified adversarial invariance"
    - "ropad robust presentation attack detection through unsupervised adversarial invariance"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "very deep convolutional networks for end to end speech recognition"
    - "listen attend and spell"
    - "attention based models for speech recognition"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "adversarial speaker adaptation"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "conditional teacher student learning"
    - "adversarial speaker verification"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "segan speech enhancement generative adversarial network"
    - "domain separation networks"
    - "maximum a posteriori adaptation of network parameters in deep models"

Connected through hyperedge HE-7 (induced by source paper "what does a network layer hear analyzing hidden representations of end to end asr through speech synthesis"):
  with members:
    - "ghostvlad for set based face recognition"
    - "voxceleb2 deep speaker recognition"
    - "tacotron towards end to end speech synthesis"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "musan a music speech and noise corpus"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving adversarial representation learning in asr reality or illusion"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "fully convolutional speech recognition"
    - "to reverse the gradient or not an empirical comparison of adversarial and multi task learning in speech recognition"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "musan a music speech and noise corpus"
    - "attention based models for speech recognition"

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)