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: "surprisal driven zoneout"
  Abstract: We propose a novel method of regularization for recurrent neural networks called suprisal-driven zoneout. In this method, states zoneout (maintain their previous value rather than updating), when the suprisal (discrepancy between the last state's prediction and target) is small. Thus regularization is adaptive and input-driven on a per-neuron basis. We demonstrate the effectiveness of this idea by achieving state-of-the-art bits per character of 1.31 on the Hutter Prize Wikipedia dataset, significantly reducing the gap to the best known highly-engineered compression methods.

Connected through hyperedge HE-1 (induced by source paper "an analysis of neural language modeling at multiple scales"):
  with members:
    - "deep contextualized word representations"
    - "learning to generate reviews and discovering sentiment"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "revisiting activation regularization for language rnns"

Connected through hyperedge HE-2 (induced by source paper "fast slow recurrent neural networks"):
  with members:
    - "adaptive computation time for recurrent neural networks"
    - "recurrent highway networks"
    - "neural architecture search with reinforcement learning"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "hierarchical multiscale recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "recurrent neural network regularization"

Connected through hyperedge HE-3 (induced by source paper "zoneout regularizing rnns by randomly preserving hidden activations"):
  with members:
    - "deep networks with stochastic depth"
    - "recurrent dropout without memory loss"
    - "recurrent batch normalization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "deep residual learning for image recognition"
    - "character aware neural language models"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

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)