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: "adversarial speaker adaptation"
  Abstract: We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model. In ASA, with a fixed SI model as the reference, an SD model is jointly optimized with the discriminator network to minimize the senone classification loss, and simultaneously to mini-maximize the SI/SD discrimination loss on the adaptation data. With ASA, a senone-discriminative deep feature is learned in the SD model with a similar distribution to that of the SI model. With such a regularized and adapted deep feature, the SD model can perform improved automatic speech recognition on the target speaker’s speech. Evaluated on the Microsoft short message dictation dataset, ASA achieves 14.4% and 7.9% relative word error rate improvements for supervised and unsupervised adaptation, respectively, over an SI model trained from 2600 hours data, with 200 adaptation utterances per speaker.

Connected through hyperedge HE-1 (induced by source paper "attentive adversarial learning for domain invariant training"):
  with members:
    - "conditional teacher student learning"
    - "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"
    - "segan speech enhancement generative adversarial network"

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 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"
    - "invariant representations for noisy speech recognition"

Connected through hyperedge HE-3 (induced by source paper "conditional teacher student learning"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "developing far field speaker system via teacher student learning"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "large scale domain adaptation via teacher student learning"
    - "sequence level knowledge distillation"
    - "knowledge distillation for small footprint highway networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial speaker verification"):
  with members:
    - "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"
    - "invariant representations for noisy 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.LG(Machine Learning)