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: "constructing long short term memory based deep recurrent neural networks for large vocabulary speech recognition"
  Abstract: Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.

Connected through hyperedge HE-1 (induced by source paper "a survey of automatic generation of source code comments algorithms and techniques"):
  with members:
    - "automatic generation of text descriptive comments for code blocks"
    - "deep reinforcement learning for sequence to sequence models"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "attention is all you need"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "code attention translating code to comments by exploiting domain features"
    - "a survey of machine learning for big code and naturalness"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "dynamic cell structure via recursive recurrent neural networks"):
  with members:
    - "layer flexible adaptive computational time for recurrent neural networks"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "a flexible approach to automated rnn architecture generation"
    - "learning transferable architectures for scalable image recognition"
    - "making neural programming architectures generalize via recursion"
    - "adaptive computation time for recurrent neural networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-3 (induced by source paper "a limited size ensemble of homogeneous cnn lstms for high performance word classification"):
  with members:
    - "a large scale field test on word image classification in large historical document collections using a traditional and two deep learning methods"
    - "handwriting recognition using cohort of lstm and lexicon verification with extremely large lexicon"
    - "character level incremental speech recognition with recurrent neural networks"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "adadelta an adaptive learning rate method"
    - "ocr post processing error correction algorithm using google online spelling suggestion"

Connected through hyperedge HE-4 (induced by source paper "two phase flow regime prediction using lstm based deep recurrent neural network"):
  with members:
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "grow and prune compact fast and accurate lstms"):
  with members:
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "image captioning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "exploring sparsity in recurrent neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "learning compact recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a multiversion programming inspired approach to detecting audio adversarial examples"):
  with members:
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "audio adversarial examples targeted attacks on speech to text"
    - "generalizable adversarial examples detection based on bi model decision mismatch"

Connected through hyperedge HE-7 (induced by source paper "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-8 (induced by source paper "deephttp semantics structure model with attention for anomalous http traffic detection and pattern mining"):
  with members:
    - "network traffic anomaly detection using recurrent neural networks"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "attention based models for speech recognition"
    - "network traffic decomposition for anomaly detection"

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)