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: "recurrent memory array structures"
  Abstract: The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves state-of-the-art performance on character level text prediction achieving 1.402 BPC on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.

Connected through hyperedge HE-1 (induced by source paper "tiresias predicting security events through deep learning"):
  with members:
    - "learning to remember rare events"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "architectural complexity measures of recurrent neural networks"
    - "character aware neural language models"

Connected through hyperedge HE-2 (induced by source paper "surprisal driven feedback in recurrent networks"):
  with members:
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "grid long short term memory"

Connected through hyperedge HE-3 (induced by source paper "hierarchical multiscale recurrent neural networks"):
  with members:
    - "surprisal driven feedback in recurrent networks"
    - "one shot learning with memory augmented neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep networks with stochastic depth"
    - "recurrent highway networks"
    - "strategic attentive writer for learning macro actions"
    - "theano a python framework for fast computation of mathematical expressions"
    - "architectural complexity measures of recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "multiplicative lstm for sequence modelling"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "on the state of the art of evaluation in neural language models"
    - "fast slow recurrent neural networks"
    - "surprisal driven feedback in recurrent networks"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "recurrent highway networks"

Connected through hyperedge HE-5 (induced by source paper "surprisal driven zoneout"):
  with members:
    - "surprisal driven feedback in recurrent networks"
    - "recurrent highway networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "on multiplicative integration with recurrent neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "grid long short term memory"

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)