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: "feedforward sequential memory networks a new structure to learn long term dependency"
  Abstract: In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback. The proposed FSMN is a standard fully-connected feedforward neural network equipped with some learnable memory blocks in its hidden layers. The memory blocks use a tapped-delay line structure to encode the long context information into a fixed-size representation as short-term memory mechanism. We have evaluated the proposed FSMNs in several standard benchmark tasks, including speech recognition and language modelling. Experimental results have shown FSMNs significantly outperform the conventional recurrent neural networks (RNN), including LSTMs, in modeling sequential signals like speech or language. Moreover, FSMNs can be learned much more reliably and faster than RNNs or LSTMs due to the inherent non-recurrent model structure.

Connected through hyperedge HE-1 (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"
    - "highway long short term memory rnns for distant speech recognition"
    - "thchs 30 a free chinese speech corpus"

Connected through hyperedge HE-2 (induced by source paper "deep fsmn for large vocabulary continuous speech recognition"):
  with members:
    - "wavenet a generative model for raw audio"
    - "achieving human parity in conversational speech recognition"
    - "feedforward sequential memory neural networks without recurrent feedback"
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep feed forward sequential memory networks for speech synthesis"):
  with members:
    - "very deep convolutional networks for end to end speech recognition"
    - "wavenet a generative model for raw audio"
    - "feedforward sequential memory neural networks without recurrent feedback"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-4 (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"
    - "highway long short term memory rnns for distant speech recognition"
    - "thchs 30 a free chinese speech corpus"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-5 (induced by source paper "empirical evaluation of speaker adaptation on dnn based acoustic model"):
  with members:
    - "the microsoft 2016 conversational speech recognition system"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "highway long short term memory rnns for distant speech recognition"
    - "the ibm 2015 english conversational telephone speech recognition system"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "end to end streaming keyword spotting"):
  with members:
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "a cascade architecture for keyword spotting on mobile devices"
    - "small footprint keyword spotting using deep neural network and connectionist temporal classifier"

Connected through hyperedge HE-7 (induced by source paper "residual memory networks feed forward approach to learn long temporal dependencies"):
  with members:
    - "deep residual learning for image recognition"
    - "feed forward networks with attention can solve some long term memory problems"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "a novel pyramidal fsmn architecture with lattice free mmi for speech recognition"):
  with members:
    - "deep fsmn for large vocabulary continuous speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"

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.NE(Neural and Evolutionary Computing)