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: "the ibm 2016 english conversational telephone speech recognition system"
  Abstract: We describe a collection of acoustic and language modeling techniques that lowered the word error rate of our English conversational telephone LVCSR system to a record 6.6% on the Switchboard subset of the Hub5 2000 evaluation testset. On the acoustic side, we use a score fusion of three strong models: recurrent nets with maxout activations, very deep convolutional nets with 3x3 kernels, and bidirectional long short-term memory nets which operate on FMLLR and i-vector features. On the language modeling side, we use an updated model "M" and hierarchical neural network LMs.

Connected through hyperedge HE-1 (induced by source paper "character level recurrent neural networks in practice comparing training and sampling schemes"):
  with members:
    - "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "improved recurrent neural networks for session based recommendations"
    - "multiplicative lstm for sequence modelling"

Connected through hyperedge HE-2 (induced by source paper "the marchex 2018 english conversational telephone speech recognition system"):
  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"
    - "semi supervised model training for unbounded conversational speech recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "the ibm 2015 english conversational telephone speech recognition system"

Connected through hyperedge HE-3 (induced by source paper "distilling knowledge using parallel data for far field speech recognition"):
  with members:
    - "a network of deep neural networks for distant speech recognition"
    - "the microsoft 2016 conversational speech recognition system"
    - "transferring knowledge from a rnn to a dnn"
    - "recurrent models for auditory attention in multi microphone distance speech recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-4 (induced by source paper "cnn lstm architecture for speech emotion recognition with data augmentation"):
  with members:
    - "gradient normalization depth based decay for deep learning"
    - "learning from simulated and unsupervised images through adversarial training"
    - "recurrent batch normalization"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-5 (induced by source paper "a comparison of lattice free discriminative training criteria for purely sequence trained neural network acoustic models"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "sequence discriminative training for deep learning based acoustic keyword spotting"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-6 (induced by source paper "simple recurrent units for highly parallelizable recurrence"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "rational recurrences"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "an analysis of neural language modeling at multiple scales"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deriving neural architectures from sequence and graph kernels"
    - "lstm a search space odyssey"
    - "recurrent additive networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning for environmentally robust speech recognition an overview of recent developments"):
  with members:
    - "generative adversarial networks an overview"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a network of deep neural networks for distant speech recognition"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "multichannel end to end speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"

Connected through hyperedge HE-8 (induced by source paper "language modeling with highway lstm"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "fast slow recurrent neural networks"
    - "comparing human and machine errors in conversational speech transcription"
    - "language modeling with gated convolutional networks"
    - "recurrent highway networks"
    - "the microsoft 2016 conversational speech recognition system"
    - "exploring the limits of language modeling"

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)