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: "lie access neural turing machine"
  Abstract: Following the recent trend in explicit neural memory structures, we present a new design of an external memory, wherein memories are stored in an Euclidean key space $\mathbb R^n$. An LSTM controller performs read and write via specialized read and write heads. It can move a head by either providing a new address in the key space (aka random access) or moving from its previous position via a Lie group action (aka Lie access). In this way, the "L" and "R" instructions of a traditional Turing Machine are generalized to arbitrary elements of a fixed Lie group action. For this reason, we name this new model the Lie Access Neural Turing Machine, or LANTM. #R##N#We tested two different configurations of LANTM against an LSTM baseline in several basic experiments. We found the right configuration of LANTM to outperform the baseline in all of our experiments. In particular, we trained LANTM on addition of $k$-digit numbers for $2 \le k \le 16$, but it was able to generalize almost perfectly to $17 \le k \le 32$, all with the number of parameters 2 orders of magnitude below the LSTM baseline.

Connected through hyperedge HE-1 (induced by source paper "memory augmented recurrent neural networks can learn generalized dyck languages"):
  with members:
    - "theoretical limitations of self attention in neural sequence models"
    - "sequential neural networks as automata"
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "attention is all you need"
    - "learning operations on a stack with neural turing machines"
    - "neural random access machines"

Connected through hyperedge HE-2 (induced by source paper "a review on neural turing machine"):
  with members:
    - "associative long short term memory"
    - "lstm with working memory"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "deep multi task learning with shared memory"
    - "terpret a probabilistic programming language for program induction"
    - "neural random access machines"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning numeracy binary arithmetic with neural turing machines"):
  with members:
    - "implementing neural turing machines"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "learning simple algorithms from examples"
    - "deep learning in neural networks"
    - "structured memory for neural turing machines"
    - "neural gpus learn algorithms"
    - "rmsprop and equilibrated adaptive learning rates for non convex optimization"
    - "neural turing machines"

Connected through hyperedge HE-4 (induced by source paper "partially non recurrent controllers for memory augmented neural networks"):
  with members:
    - "robust and scalable differentiable neural computer for question answering"
    - "memory augmented neural networks with wormhole connections"
    - "tracking the world state with recurrent entity networks"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "wavenet a generative model for raw audio"
    - "learning to transduce with unbounded memory"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "reinforcement learning neural turing machines revised"

Connected through hyperedge HE-5 (induced by source paper "dynamic neural turing machine with soft and hard addressing schemes"):
  with members:
    - "noisy activation functions"
    - "recurrent dropout without memory loss"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "dynamic memory networks for visual and textual question answering"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "recurrent batch normalization"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "end to end memory networks"

Connected through hyperedge HE-6 (induced by source paper "learning to optimize"):
  with members:
    - "neural random access machines"
    - "learning simple algorithms from examples"
    - "neural programmer interpreters"
    - "learning contact rich manipulation skills with guided policy search"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "neural gpus learn algorithms"
    - "deep spatial autoencoders for visuomotor learning"
    - "reinforcement learning neural turing machines revised"

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)