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: "small footprint deep neural networks with highway connections for speech recognition"
  Abstract: For speech recognition, deep neural networks (DNNs) have significantly improved the recognition accuracy in most of benchmark datasets and application domains. However, compared to the conventional Gaussian mixture models, DNN-based acoustic models usually have much larger number of model parameters, making it challenging for their applications in resource constrained platforms, e.g., mobile devices. In this paper, we study the application of the recently proposed highway network to train small-footprint DNNs, which are {\it thinner} and {\it deeper}, and have significantly smaller number of model parameters compared to conventional DNNs. We investigated this approach on the AMI meeting speech transcription corpus which has around 70 hours of audio data. The highway neural networks constantly outperformed their plain DNN counterparts, and the number of model parameters can be reduced significantly without sacrificing the recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "multi task sentence encoding model for semantic retrieval in question answering systems"):
  with members:
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "natural language inference over interaction space"
    - "attention is all you need"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a structured self attentive sentence embedding"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning to compose task specific tree structures"

Connected through hyperedge HE-2 (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"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-3 (induced by source paper "small footprint highway deep neural networks for speech recognition"):
  with members:
    - "knowledge distillation for small footprint highway networks"
    - "sequence training and adaptation of highway deep neural 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"

Connected through hyperedge HE-4 (induced by source paper "toward computation and memory efficient neural network acoustic models with binary weights and activations"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "knowledge distillation for small footprint highway networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "highway and residual networks learn unrolled iterative estimation"):
  with members:
    - "visualizing residual networks"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "recurrent highway networks"
    - "fully character level neural machine translation without explicit segmentation"

Connected through hyperedge HE-6 (induced by source paper "knowledge distillation for small footprint highway networks"):
  with members:
    - "sequence training and adaptation of highway deep neural networks"
    - "acdc a structured efficient linear layer"
    - "training very deep networks"
    - "fitnets hints for thin deep nets"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-7 (induced by source paper "sequence training and adaptation of highway deep neural networks"):
  with members:
    - "acdc a structured efficient linear layer"
    - "highway long short term memory rnns for distant speech recognition"
    - "training very deep networks"
    - "fitnets hints for thin deep nets"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-8 (induced by source paper "composition of deep and spiking neural networks for very low bit rate speech coding"):
  with members:
    - "speech vocoding for laboratory phonology"
    - "on structured sparsity of phonological posteriors for linguistic parsing"

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)