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: "advances in very deep convolutional neural networks for lvcsr"
  Abstract: Very deep CNNs with small 3x3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we investigate how to efficiently scale these models to larger datasets. Specifically, we address the design choice of pooling and padding along the time dimension which renders convolutional evaluation of sequences highly inefficient. We propose a new CNN design without timepadding and without timepooling, which is slightly suboptimal for accuracy, but has two significant advantages: it enables sequence training and deployment by allowing efficient convolutional evaluation of full utterances, and, it allows for batch normalization to be straightforwardly adopted to CNNs on sequence data. Through batch normalization, we recover the lost peformance from removing the time-pooling, while keeping the benefit of efficient convolutional evaluation. We demonstrate the performance of our models both on larger scale data than before, and after sequence training. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of the 2015 IBM system combination, which was the previous best published result.

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 "multi scale octave convolutions for robust speech recognition"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "big little net an efficient multi scale feature representation for visual and speech recognition"
    - "condensenet an efficient densenet using learned group convolutions"
    - "very deep convolutional neural networks for robust speech recognition"
    - "pruning filters for efficient convnets"
    - "very deep multilingual convolutional neural networks for lvcsr"

Connected through hyperedge HE-3 (induced by source paper "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"):
  with members:
    - "lstm a search space odyssey"
    - "improved recurrent neural networks for session based recommendations"
    - "item2vec neural item embedding for collaborative filtering"
    - "from word embeddings to item recommendation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "session based recommendations with recurrent neural networks"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "analyzing deep cnn based utterance embeddings for acoustic model adaptation"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "the microsoft 2017 conversational speech recognition system"
    - "achieving human parity in conversational speech recognition"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "residual convolutional ctc networks for automatic speech recognition"):
  with members:
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "very deep convolutional networks for end to end speech recognition"
    - "very deep convolutional neural networks for robust speech recognition"
    - "deep recurrent convolutional neural network improving performance for speech recognition"
    - "wide residual networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "empirical evaluation of parallel training algorithms on acoustic modeling"):
  with members:
    - "theano mpi a theano based distributed training framework"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "experiments on parallel training of deep neural network using model averaging"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "parallel training of dnns with natural gradient and parameter averaging"
    - "gpu asynchronous stochastic gradient descent to speed up neural network training"

Connected through hyperedge HE-7 (induced by source paper "end to end attention based text dependent speaker verification"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "end to end text dependent speaker verification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a unified deep neural network for speaker and language recognition"
    - "the ibm 2015 english conversational telephone speech recognition system"
    - "very deep convolutional networks for large scale image recognition"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-8 (induced by source paper "very deep convolutional networks for end to end speech recognition"):
  with members:
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "end to end attention based large vocabulary speech recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "task loss estimation for sequence prediction"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "grid long short term memory"
    - "attention based models for speech recognition"

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)