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: "extensions and limitations of the neural gpu"
  Abstract: The Neural GPU is a recent model that can learn algorithms such as multi-digit binary addition and binary multiplication in a way that generalizes to inputs of arbitrary length.  We show that there are two simple ways of improving the performance of the Neural GPU: by carefully designing a curriculum, and by increasing model size.  The latter requires a memory efficient implementation, as a naive implementation of the Neural GPU is memory intensive.  We find that these techniques increase the set of algorithmic problems that can be solved by the Neural GPU: we have been able to learn to perform all the arithmetic operations (and generalize to arbitrarily long numbers) when the arguments are given in the decimal representation (which, surprisingly, has not been possible before). We have also been able to train the Neural GPU to evaluate long arithmetic expressions with multiple operands that require respecting the  precedence order of the operands, although these have succeeded only in their binary representation, and not with perfect accuracy.#N##N#In addition, we gain insight into the Neural GPU by investigating its failure modes.  We find that Neural GPUs that correctly generalize to arbitrarily long numbers still fail to compute the correct answer on highly-symmetric, atypical inputs: for  example, a Neural GPU that achieves near-perfect generalization on decimal multiplication of up to 100-digit long numbers can fail on $000000\dots002 \times 000000\dots002$ while succeeding at $2 \times 2$.  These failure modes are reminiscent of adversarial examples.

Connected through hyperedge HE-1 (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"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"
    - "improving the neural gpu architecture for algorithm learning"
    - "identity mappings in deep residual networks"
    - "neural random access machines"

Connected through hyperedge HE-2 (induced by source paper "attending to mathematical language with transformers"):
  with members:
    - "universal transformers"
    - "tensor2tensor for neural machine translation"
    - "attention is all you need"
    - "improving the neural gpu architecture for algorithm learning"
    - "adaptive computation time for recurrent neural networks"
    - "grid long short term memory"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "visual learning of arithmetic operations"

Connected through hyperedge HE-3 (induced by source paper "improving the neural gpu architecture for algorithm learning"):
  with members:
    - "learning efficient algorithms with hierarchical attentive memory"
    - "recurrent dropout without memory loss"
    - "neural random access machines"
    - "learning simple algorithms from examples"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "learning to transduce with unbounded memory"
    - "grid long short term memory"
    - "neural programmer interpreters"

Connected through hyperedge HE-4 (induced by source paper "differentiable programs with neural libraries"):
  with members:
    - "progressive neural networks"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "understanding deep learning requires rethinking generalization"
    - "adaptive neural compilation"
    - "programming with a differentiable forth interpreter"
    - "terpret a probabilistic programming language for program induction"
    - "neural random access machines"
    - "neural programmer inducing latent programs with gradient descent"

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)