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: "robust speech recognition using generative adversarial networks"
  Abstract: This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained with the proposed approach enjoy improved invariance by learning to map noisy audio to the same embedding space as that of clean audio. Unlike previous methods, the new framework does not rely on domain expertise or simplifying assumptions as are often needed in signal processing, and directly encourages robustness in a data-driven way. We show the new approach improves simulated far-field speech recognition of vanilla sequence-to-sequence models without specialized front-ends or preprocessing.

Connected through hyperedge HE-1 (induced by source paper "adversarial invariant feature learning with accuracy constraint for domain generalization"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "domain generalization via conditional invariant representation"
    - "generalizing across domains via cross gradient training"
    - "learning adversarially fair and transferable representations"
    - "learning to generalize meta learning for domain generalization"
    - "unified deep supervised domain adaptation and generalization"
    - "controllable invariance through adversarial feature learning"
    - "deeper broader and artier domain generalization"

Connected through hyperedge HE-2 (induced by source paper "speech recognition with no speech or with noisy speech"):
  with members:
    - "neural networks based eeg speech models"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "characterizing audio adversarial examples using temporal dependency"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "audio adversarial examples targeted attacks on speech to text"
    - "on the limitation of magnet defense against l_1 based adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "mtgan speaker verification through multitasking triplet generative adversarial networks"):
  with members:
    - "representation learning by rotating your faces"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "training triplet networks with gan"
    - "deep speaker an end to end neural speaker embedding system"
    - "improved training of wasserstein gans"
    - "tripletgan training generative model with triplet loss"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-5 (induced by source paper "receiver placement for speech enhancement using sound propagation optimization"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"

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)