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: "end to end phoneme sequence recognition using convolutional neural networks"
  Abstract: Note: Accepted at NIPS Deep learning Workshop Reference EPFL-REPORT-192559 Record created on 2013-12-19, modified on 2017-05-10

Connected through hyperedge HE-1 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "espnet end to end speech processing toolkit"
    - "end to end speech recognition from the raw waveform"
    - "improving speech recognition by revising gated recurrent units"
    - "deep complex networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-2 (induced by source paper "end to end speech recognition from the raw waveform"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "the microsoft 2017 conversational speech recognition system"
    - "improving end to end speech recognition with policy learning"
    - "learning filterbanks from raw speech for phone recognition"
    - "sequence to sequence asr optimization via reinforcement learning"
    - "wav2letter an end to end convnet based speech recognition system"
    - "attention based wav2text with feature transfer learning"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "attention based wav2text with feature transfer learning"):
  with members:
    - "wav2letter an end to end convnet based speech recognition system"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "striving for simplicity the all convolutional net"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"

Connected through hyperedge HE-4 (induced by source paper "learning filterbanks from raw speech for phone recognition"):
  with members:
    - "wav2letter an end to end convnet based speech recognition system"
    - "attention based wav2text with feature transfer learning"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "wavenet a generative model for raw audio"
    - "achieving human parity in conversational speech recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "network in network"

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.LG(Machine Learning)