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: "phoneme recognition in timit with blstm ctc"
  Abstract: We compare the performance of a recurrent neural network with the best results published so far on phoneme recognition in the TIMIT database. These published results have been obtained with a combination of classifiers. However, in this paper we apply a single recurrent neural network to the same task. Our recurrent neural network attains an error rate of 24.6%. This result is not significantly different from that obtained by the other best methods, but they rely on a combination of classifiers for achieving comparable performance.

Connected through hyperedge HE-1 (induced by source paper "understanding lstm a tutorial into long short term memory recurrent neural networks"):
  with members:
    - "listen attend and spell"
    - "grid long short term memory"
    - "attention based models for speech recognition"
    - "a neural conversational model"
    - "neural turing machines"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "a clockwork rnn"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "sampling from stochastic finite automata with applications to ctc decoding"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "object recognition from short videos for robotic perception"):
  with members:
    - "lstm a search space odyssey"
    - "monocular slam supported object recognition"
    - "beyond short snippets deep networks for video classification"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "deep learning for detecting robotic grasps"
    - "speech recognition with deep recurrent neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "beyond short snippets deep networks for video classification"):
  with members:
    - "going deeper with convolutions"
    - "learning to execute"
    - "two stream convolutional networks for action recognition in videos"
    - "visualizing and understanding convolutional networks"
    - "speech recognition with deep recurrent neural networks"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

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)