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: "thchs 30 a free chinese speech corpus"
  Abstract: Speech data is crucially important for speech recognition research. There are quite some speech databases that can be purchased at prices that are reasonable for most research institutes. However, for young people who just start research activities or those who just gain initial interest in this direction, the cost for data is still an annoying barrier. We support the `free data' movement in speech recognition: research institutes (particularly supported by public funds) publish their data freely so that new researchers can obtain sufficient data to kick of their career. In this paper, we follow this trend and release a free Chinese speech database THCHS-30 that can be used to build a full- edged Chinese speech recognition system. We report the baseline system established with this database, including the performance under highly noisy conditions.

Connected through hyperedge HE-1 (induced by source paper "universal phone recognition with a multilingual allophone system"):
  with members:
    - "towards zero shot learning for automatic phonemic transcription"
    - "multilingual speech recognition with corpus relatedness sampling"
    - "towards a general purpose linguistic annotation backend"
    - "sequence based multi lingual low resource speech recognition"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"

Connected through hyperedge HE-2 (induced by source paper "multilingual speech recognition with corpus relatedness sampling"):
  with members:
    - "domain robust feature extraction for rapid low resource asr development"
    - "sequence based multi lingual low resource speech recognition"
    - "enriching word vectors with subword information"
    - "a curriculum learning method for improved noise robustness in automatic speech recognition"
    - "achieving human parity in conversational speech recognition"
    - "character aware neural language models"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "atcspeech a multilingual pilot controller speech corpus from real air traffic control environment"):
  with members:
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "wav2letter the fastest open source speech recognition system"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "the airbus air traffic control speech recognition 2018 challenge towards atc automatic transcription and call sign detection"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"

Connected through hyperedge HE-4 (induced by source paper "improving gated recurrent unit based acoustic modeling with batch normalization and enlarged context"):
  with members:
    - "deep fsmn for large vocabulary continuous speech recognition"
    - "gated recurrent unit based acoustic modeling with future context"
    - "improving speech recognition by revising gated recurrent units"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "recurrent batch normalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-5 (induced by source paper "investigation of using disentangled and interpretable representations for one shot cross lingual voice conversion"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "deep convolutional inverse graphics network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "gated recurrent unit based acoustic modeling with future context"):
  with members:
    - "improving speech recognition by revising gated recurrent units"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "highway long short term memory rnns for distant speech recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-7 (induced by source paper "ap18 olr challenge three tasks and their baselines"):
  with members:
    - "ap17 olr challenge data plan and baseline"
    - "ap16 ol7 a multilingual database for oriental languages and a language recognition baseline"
    - "transfer learning for speech and language processing"

Connected through hyperedge HE-8 (induced by source paper "aishell 2 transforming mandarin asr research into industrial scale"):
  with members:
    - "gated recurrent unit based acoustic modeling with future context"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"

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)