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: "sequence training and adaptation of highway deep neural networks"
  Abstract: Highway deep neural network (HDNN) is a type of depth-gated feedforward neural network, which has shown to be easier to train with more hidden layers and also generalise better compared to conventional plain deep neural networks (DNNs). Previously, we investigated a structured HDNN architecture for speech recognition, in which the two gate functions were tied across all the hidden layers, and we were able to train a much smaller model without sacrificing the recognition accuracy. In this paper, we carry on the study of this architecture with sequence-discriminative training criterion and speaker adaptation techniques on the AMI meeting speech recognition corpus. We show that these two techniques improve speech recognition accuracy on top of the model trained with the cross entropy criterion. Furthermore, we demonstrate that the two gate functions that are tied across all the hidden layers are able to control the information flow over the whole network, and we can achieve considerable improvements by only updating these gate functions in both sequence training and adaptation experiments.

Connected through hyperedge HE-1 (induced by source paper "the capio 2017 conversational speech recognition system"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "the microsoft 2017 conversational speech recognition system"
    - "an improved residual lstm architecture for acoustic modeling"
    - "language modeling with highway lstm"
    - "comparing human and machine errors in conversational speech transcription"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "small footprint highway deep neural networks for speech recognition"):
  with members:
    - "knowledge distillation for small footprint highway networks"
    - "recurrent highway networks"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "acdc a structured efficient linear layer"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "transferring knowledge from a rnn to a dnn"

Connected through hyperedge HE-3 (induced by source paper "knowledge distillation for small footprint highway networks"):
  with members:
    - "acdc a structured efficient linear layer"
    - "small footprint deep neural networks with highway connections for speech recognition"
    - "training very deep networks"
    - "fitnets hints for thin deep nets"
    - "do deep nets really need to be deep"

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)