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: "learning hidden unit contributions for unsupervised acoustic model adaptation"
  Abstract: This work presents a broad study on the adaptation of neural network acoustic models by means of learning hidden unit contributions (LHUC) ---a method that linearly re-combines hidden units in a speaker- or environment-dependent manner using small amounts of unsupervised adaptation data. We also extend LHUC to a speaker adaptive training (SAT) framework that leads to a more adaptable DNN acoustic model, working both in a speaker-dependent and a speaker-independent manner, without the requirements to maintain auxiliary speaker-dependent feature extractors or to introduce significant speaker-dependent changes to the DNN structure. Through a series of experiments on four different speech recognition benchmarks (TED talks, Switchboard, AMI meetings, and Aurora4) comprising 270 test speakers, we show that LHUC in both its test-only and SAT variants results in consistent word error rate reductions ranging from 5% to 23% relative depending on the task and the degree of mismatch between training and test data. In addition, we have investigated the effect of the amount of adaptation data per speaker, the quality of unsupervised adaptation targets, the complementarity to other adaptation techniques, one-shot adaptation, and an extension to adapting DNNs trained in a sequence discriminative manner.

Connected through hyperedge HE-1 (induced by source paper "speaker adaptation for end to end ctc models"):
  with members:
    - "advancing acoustic to word ctc model"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "direct acoustics to word models for english conversational speech recognition"
    - "embedding based speaker adaptive training of deep neural networks"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"

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 "conditional teacher student learning"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "speaker adaptive training using model agnostic meta learning"):
  with members:
    - "learning to adapt a meta learning approach for speaker adaptation"
    - "meta learning for low resource neural machine translation"
    - "how to train your maml"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "embeddings for dnn speaker adaptive training"):
  with members:
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "voxceleb2 deep speaker recognition"
    - "dynamic layer normalization for adaptive neural acoustic modeling in speech recognition"
    - "embedding based speaker adaptive training of deep neural networks"
    - "environmental noise embeddings for robust speech recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "lattice based unsupervised test time adaptation of neural network acoustic models"):
  with members:
    - "semi supervised model training for unbounded conversational speech recognition"
    - "dnn adaptation by automatic quality estimation of asr hypotheses"

Connected through hyperedge HE-7 (induced by source paper "semi tied units for efficient gating in lstm and highway networks"):
  with members:
    - "improved tdnns using deep kernels and frequency dependent grid rnns"
    - "high order recurrent neural networks for acoustic modelling"
    - "very deep convolutional networks for end to end speech recognition"
    - "quasi recurrent neural networks"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "small footprint deep neural networks with highway connections for speech recognition"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-8 (induced by source paper "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"):
  with members:
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "tackling over pruning in variational autoencoders"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning latent representations for speech generation and transformation"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "highway long short term memory rnns for distant 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)