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: "neural random access machines"
  Abstract: In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine. It can manipulate and dereference pointers to an external variable-size random-access memory. The model is trained from pure input-output examples using backpropagation. #R##N#We evaluate the new model on a number of simple algorithmic tasks whose solutions require pointer manipulation and dereferencing. Our results show that the proposed model can learn to solve algorithmic tasks of such type and is capable of operating on simple data structures like linked-lists and binary trees. For easier tasks, the learned solutions generalize to sequences of arbitrary length. Moreover, memory access during inference can be done in a constant time under some assumptions.

Connected through hyperedge HE-1 (induced by source paper "towards modular algorithm induction"):
  with members:
    - "modular networks learning to decompose neural computation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "automatically composing representation transformations as a means for generalization"
    - "robustfill neural program learning under noisy i o"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "deepcoder learning to write programs"
    - "learning simple algorithms from examples"

Connected through hyperedge HE-2 (induced by source paper "progress extrapolating algorithmic learning to arbitrary sequence lengths"):
  with members:
    - "universal transformers"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "improving the neural gpu architecture for algorithm learning"
    - "making neural programming architectures generalize via recursion"
    - "adaptive computation time for recurrent neural networks"
    - "neural programmer interpreters"
    - "neural gpus learn algorithms"
    - "neural turing machines"

Connected through hyperedge HE-3 (induced by source paper "graph element networks adaptive structured computation and memory"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "attentive neural processes"
    - "a comprehensive survey on graph neural networks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "graph neural networks a review of methods and applications"
    - "modular meta learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"

Connected through hyperedge HE-4 (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-5 (induced by source paper "multigrid neural memory"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "attention is all you need"
    - "neural map structured memory for deep reinforcement learning"
    - "generative temporal models with memory"
    - "one shot learning with memory augmented neural networks"
    - "multigrid neural architectures"
    - "asynchronous methods for deep reinforcement learning"
    - "control of memory active perception and action in minecraft"

Connected through hyperedge HE-6 (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"
    - "lie access neural turing machine"
    - "learning operations on a stack with neural turing machines"

Connected through hyperedge HE-7 (induced by source paper "a review on neural turing machine"):
  with members:
    - "associative long short term memory"
    - "lstm with working memory"
    - "lie access neural turing machine"
    - "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"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "armin towards a more efficient and light weight recurrent memory network"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "towards binary valued gates for robust lstm training"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "fast slow recurrent neural networks"
    - "memory augmented neural networks with wormhole connections"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "associative long short term memory"
    - "scaling memory augmented neural networks with sparse reads and writes"

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.LG(Machine Learning)