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: "modeling compositionality with multiplicative recurrent neural networks"
  Abstract: We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform comparably or better than Elman-type additive recurrent neural networks and outperform matrix-space models on a standard fine-grained sentiment analysis corpus. Furthermore, they yield comparable results to structural deep models on the recently published Stanford Sentiment Treebank without the need for generating parse trees.

Connected through hyperedge HE-1 (induced by source paper "logic and the 2 simplicial transformer"):
  with members:
    - "neural logic machines"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "an investigation of model free planning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational recurrent neural networks"
    - "generalisation of structural knowledge in the hippocampal entorhinal system"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-2 (induced by source paper "sex trafficking detection with ordinal regression neural networks"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "insight 1 at semeval 2016 task 4 convolutional neural networks for sentiment classification and quantification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "gated feedback recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "a way out of the odyssey analyzing and combining recent insights for lstms"):
  with members:
    - "lstm a search space odyssey"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "deep networks with stochastic depth"
    - "dynamic memory networks for visual and textual question answering"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "neural tree indexers for text understanding"
    - "improved semantic representations from tree structured long short term memory networks"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-4 (induced by source paper "restricted recurrent neural tensor networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "exploring the limits of language modeling"
    - "conditional computation in neural networks for faster models"
    - "strategies for training large vocabulary neural language models"
    - "recurrent neural network regularization"

Connected through hyperedge HE-5 (induced by source paper "neural tree indexers for text understanding"):
  with members:
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "neural architectures for named entity recognition"
    - "neural variational inference for text processing"
    - "long short term memory over tree structures"
    - "natural language inference by tree based convolution and heuristic matching"
    - "improved semantic representations from tree structured long short term memory networks"
    - "learning natural language inference with lstm"

Connected through hyperedge HE-6 (induced by source paper "gated recurrent neural tensor network"):
  with members:
    - "long short term memory over tree structures"
    - "gated feedback recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning longer memory in recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "ask me anything dynamic memory networks for natural language processing"):
  with members:
    - "learning to compose neural networks for question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "stacked attention networks for image question answering"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "teaching machines to read and comprehend"
    - "neural gpus learn algorithms"

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)