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 neural networks without recurrent feedback"
  Abstract: We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback. The proposed FSMN is a standard feedforward neural networks equipped with learnable sequential memory blocks in the hidden layers. In this work, we have applied FSMN to several language modeling (LM) tasks. Experimental results have shown that the memory blocks in FSMN can learn effective representations of long history. Experiments have shown that FSMN based language models can significantly outperform not only feedforward neural network (FNN) based LMs but also the popular recurrent neural network (RNN) LMs.

Connected through hyperedge HE-1 (induced by source paper "transformer based acoustic modeling for hybrid speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "deja vu double feature presentation in deep transformer networks"
    - "a comparative study on transformer vs rnn in speech applications"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "streaming end to end speech recognition for mobile devices"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "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"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep residual learning for image recognition"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "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 "on training bi directional neural network language model with noise contrastive estimation"):
  with members:
    - "bidirectional recurrent neural networks as generative models reconstructing gaps in time series"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "recurrent neural network regularization"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-5 (induced by source paper "feedforward sequential memory networks a new structure to learn long term dependency"):
  with members:
    - "end to end memory networks"
    - "deep learning in neural networks"
    - "character aware neural language models"
    - "hybrid orthogonal projection and estimation hope a new framework to probe and learn neural networks"
    - "neural turing machines"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

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)