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 "improving the neural gpu architecture for algorithm learning"):
  Abstract: Algorithm learning is a core problem in artificial intelligence with significant implications on automation level that can be achieved by machines. Recently deep learning methods are emerging for synthesizing an algorithm from its input-output examples, the most successful being the Neural GPU, capable of learning multiplication. We present several improvements to the Neural GPU that substantially reduces training time and improves generalization. We introduce a new technique - hard nonlinearities with saturation costs- that has general applicability. We also introduce a technique of diagonal gates that can be applied to active-memory models. The proposed architecture is the first capable of learning decimal multiplication end-to-end.

Includes members:
  Member "extensions and limitations of the neural gpu":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the turing completeness of modern neural network architectures")
  Member "learning efficient algorithms with hierarchical attentive memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grammar filtering for syntax guided synthesis")
  Member "recurrent dropout without memory loss":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "neural random access machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards modular algorithm induction")
  Member "learning simple algorithms from examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards modular algorithm induction")
  Member "deep speech 2 end to end speech recognition in english and mandarin":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  Member "learning to transduce with unbounded memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "arma nets expanding receptive field for dense prediction")

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)