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: "learning operations on a stack with neural turing machines"
  Abstract: Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed recently to address the difficulty of storing information over long time periods. In this paper, we experiment with the capacity of Neural Turing Machines (NTMs) to deal with these long-term dependencies on well-balanced strings of parentheses. We show that not only does the NTM emulate a stack with its heads and learn an algorithm to recognize such words, but it is also capable of strongly generalizing to much longer sequences.

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"
    - "lie access neural turing machine"
    - "neural random access machines"

Connected through hyperedge HE-2 (induced by source paper "lstm networks can perform dynamic counting"):
  with members:
    - "context free transductions with neural stacks"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "evaluating the ability of lstms to learn context free grammars"
    - "learning to transduce with unbounded memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for de novo drug design"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "multi step reinforcement learning a unifying algorithm"
    - "molecular de novo design through deep reinforcement learning"
    - "modelling chemical reasoning to predict and invent reactions"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"
    - "low data drug discovery with one shot learning"
    - "learning to transduce with unbounded memory"
    - "visualizing and understanding recurrent networks"

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)