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: "maximum a posteriori adaptation of network parameters in deep models"
  Abstract: We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance. Given an abundance of DNN parameters but with only a limited amount of data, the effectiveness of the adapted DNN model can often be compromised. We formulate maximum a posteriori (MAP) adaptation of parameters of a specially designed CD-DNN-HMM with an augmented linear hidden networks connected to the output tied states, or senones, and compare it to feature space MAP linear regression previously proposed. Experimental evidences on the 20,000-word open vocabulary Wall Street Journal task demonstrate the feasibility of the proposed framework. In supervised adaptation, the proposed MAP adaptation approach provides more than 10% relative error reduction and consistently outperforms the conventional transformation based methods. Furthermore, we present an initial attempt to generate hierarchical priors to improve adaptation efficiency and effectiveness with limited adaptation data by exploiting similarities among senones.

Connected through hyperedge HE-1 (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-2 (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"
    - "invariant representations for noisy speech recognition"

Connected through hyperedge HE-3 (induced by source paper "developing far field speaker system via teacher student learning"):
  with members:
    - "advancing acoustic to word ctc model"
    - "advancing connectionist temporal classification with attention modeling"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "large scale domain adaptation via teacher student learning"
    - "transferring knowledge from a rnn to a dnn"

Connected through hyperedge HE-4 (induced by source paper "learning to adapt a meta learning approach for speaker adaptation"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "labeled memory networks for online model adaptation"):
  with members:
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "matching networks for one shot learning"
    - "improving neural language models with a continuous cache"
    - "pointer sentinel mixture models"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-6 (induced by source paper "differentiable pooling for unsupervised acoustic model adaptation"):
  with members:
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "learned norm pooling for deep feedforward and recurrent neural networks"
    - "improvements to deep convolutional neural networks for lvcsr"
    - "feature learning in deep neural networks studies on speech recognition tasks"
    - "differentiable pooling for hierarchical feature learning"
    - "convolutional neural networks applied to house numbers digit classification"

Connected through hyperedge HE-7 (induced by source paper "learning hidden unit contributions for unsupervised acoustic model adaptation"):
  with members:
    - "improvements to deep convolutional neural networks for lvcsr"

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)