human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lstm with working memory"):
  Abstract: LSTM is arguably the most successful RNN architecture for many tasks that involve sequential information. In the past few years there have been several proposed improvements to LSTM. We propose an improvement to LSTM which allows communication between memory cells in different blocks and allows an LSTM layer to carry out internal computation within its memory.

Includes members:
  Member "associative long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep quaternion features for privacy protection")
  Member "adaptive computation time for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention over parameters for dialogue systems")
  Member "a simple way to initialize recurrent networks of rectified linear units":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "unitary evolution recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "encoding based memory modules for recurrent neural networks")
  Member "grid long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arma nets expanding receptive field for dense prediction")
  Member "training very deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "regularizing rnns by stabilizing activations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)