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: "language modeling with highway lstm"
  Abstract: Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks. In this paper, we extend an LSTM by adding highway networks inside an LSTM and use the resulting Highway LSTM (HW-LSTM) model for language modeling. The added highway networks increase the depth in the time dimension. Since a typical LSTM has two internal states, a memory cell and a hidden state, we compare various types of HW-LSTM by adding highway networks onto the memory cell and/or the hidden state. Experimental results on English broadcast news and conversational telephone speech recognition show that the proposed HW-LSTM LM improves speech recognition accuracy on top of a strong LSTM LM baseline. We report 5.1% and 9.9% on the Switchboard and CallHome subsets of the Hub5 2000 evaluation, which reaches the best performance numbers reported on these tasks to date.

Connected through hyperedge HE-1 (induced by source paper "english broadcast news speech recognition by humans and machines"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "regularizing and optimizing lstm language models"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "improvements to deep convolutional neural networks for lvcsr"

Connected through hyperedge HE-2 (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"
    - "comparing human and machine errors in conversational speech transcription"
    - "sequence training and adaptation of highway deep neural networks"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-3 (induced by source paper "investigating generative adversarial networks based speech dereverberation for robust speech recognition"):
  with members:
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "the microsoft 2017 conversational speech recognition system"
    - "segan speech enhancement generative adversarial network"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"
    - "a fully convolutional neural network for speech enhancement"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"

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)