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: "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.

Connected through hyperedge HE-1 (induced by source paper "progress extrapolating algorithmic learning to arbitrary sequence lengths"):
  with members:
    - "universal transformers"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "making neural programming architectures generalize via recursion"
    - "adaptive computation time for recurrent neural networks"
    - "neural random access machines"
    - "neural programmer interpreters"
    - "neural gpus learn algorithms"
    - "neural turing machines"

Connected through hyperedge HE-2 (induced by source paper "neural arithmetic units"):
  with members:
    - "learning dexterous in hand manipulation"
    - "neural arithmetic expression calculator"
    - "neural arithmetic logic units"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on evaluating the generalization of lstm models in formal languages"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "grid long short term memory"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "inalu improved neural arithmetic logic unit"):
  with members:
    - "flow based network traffic generation using generative adversarial networks"
    - "measuring arithmetic extrapolation performance"
    - "neural arithmetic expression calculator"
    - "neural arithmetic logic units"
    - "grid long short term memory"
    - "neural programmer interpreters"
    - "adam a method for stochastic optimization"
    - "learning to execute"

Connected through hyperedge HE-4 (induced by source paper "towards finding longer proofs"):
  with members:
    - "enigma ng efficient neural and gradient boosted inference guidance for e"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "meta learning of sequential strategies"
    - "grunge a grand unified atp challenge"
    - "analysing mathematical reasoning abilities of neural models"
    - "graph representations for higher order logic and theorem proving"
    - "learning to speak and act in a fantasy text adventure game"
    - "hammering mizar by learning clause guidance"

Connected through hyperedge HE-5 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-6 (induced by source paper "is attention all what you need an empirical investigation on convolution based active memory and self attention"):
  with members:
    - "augmenting self attention with persistent memory"
    - "pay less attention with lightweight and dynamic convolutions"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "r transformer recurrent neural network enhanced transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "modeling recurrence for transformer"

Connected through hyperedge HE-7 (induced by source paper "on the turing completeness of modern neural network architectures"):
  with members:
    - "universal transformers"
    - "self attention with relative position representations"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "extensions and limitations of the neural gpu"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"
    - "identity mappings in deep residual networks"
    - "neural random access machines"

Connected through hyperedge HE-8 (induced by source paper "measuring arithmetic extrapolation performance"):
  with members:
    - "neural arithmetic logic units"
    - "on evaluating the generalization of lstm models in formal languages"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "grid long short term memory"
    - "neural gpus learn algorithms"
    - "adam a method for stochastic optimization"
    - "learning to execute"

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)