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: "very deep convolutional neural networks for robust speech recognition"
  Abstract: This paper describes the extension and optimization of our previous work on very deep convolutional neural networks (CNNs) for effective recognition of noisy speech in the Aurora 4 task. The appropriate number of convolutional layers, the sizes of the filters, pooling operations and input feature maps are all modified: the filter and pooling sizes are reduced and dimensions of input feature maps are extended to allow adding more convolutional layers. Furthermore appropriate input padding and input feature map selection strategies are developed. In addition, an adaptation framework using joint training of very deep CNN with auxiliary features i-vector and fMLLR features is developed. These modifications give substantial word error rate reductions over the standard CNN used as baseline. Finally the very deep CNN is combined with an LSTM-RNN acoustic model and it is shown that state-level weighted log likelihood score combination in a joint acoustic model decoding scheme is very effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%, further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN joint decoding.

Connected through hyperedge HE-1 (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"
    - "advances in very deep convolutional neural networks for lvcsr"
    - "pruning filters for efficient convnets"
    - "very deep multilingual convolutional neural networks for lvcsr"

Connected through hyperedge HE-2 (induced by source paper "an exploration of mimic architectures for residual network based spectral mapping"):
  with members:
    - "spectral feature mapping with mimic loss for robust speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "very deep convolutional networks for end to end speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "the microsoft 2016 conversational speech recognition system"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "improved tdnns using deep kernels and frequency dependent grid rnns"):
  with members:
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "very deep convolutional networks for large scale image recognition"
    - "finding consensus in speech recognition word error minimization and other applications of confusion networks"

Connected through hyperedge HE-4 (induced by source paper "spectral feature mapping with mimic loss for robust speech recognition"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "single channel multi talker speech recognition with permutation invariant training"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "deep attractor network for single microphone speaker separation"
    - "recognizing multi talker speech with permutation invariant training"
    - "dense prediction on sequences with time dilated convolutions for speech recognition"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "single channel multi speaker separation using deep clustering"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "the microsoft 2016 conversational speech recognition system"

Connected through hyperedge HE-6 (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"
    - "deep recurrent convolutional neural network improving performance for speech recognition"
    - "advances in very deep convolutional neural networks for lvcsr"
    - "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-7 (induced by source paper "recognizing multi talker speech with permutation invariant training"):
  with members:
    - "deep attractor network for single microphone speaker separation"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "single channel multi speaker separation using deep clustering"
    - "achieving human parity in conversational speech recognition"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-8 (induced by source paper "deep recurrent convolutional neural network improving performance for speech recognition"):
  with members:
    - "identity mappings in deep residual networks"
    - "nn grams unifying neural network and n gram language models for speech recognition"
    - "advances in very deep convolutional neural networks for lvcsr"
    - "online sequence training of recurrent neural networks with connectionist temporal classification"
    - "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"
    - "fast and accurate deep network learning by exponential linear units elus"

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)