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: "efficient character level document classification by combining convolution and recurrent layers"
  Abstract: Document classification tasks were primarily tackled at word level. Recent research that works with character-level inputs shows several benefits over word-level approaches such as natural incorporation of morphemes and better handling of rare words. We propose a neural network architecture that utilizes both convolution and recurrent layers to efficiently encode character inputs. We validate the proposed model on eight large scale document classification tasks and compare with character-level convolution-only models. It achieves comparable performances with much less parameters.

Connected through hyperedge HE-1 (induced by source paper "hierarchical models vs transfer learning for document level sentiment classification"):
  with members:
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "language model pre training for hierarchical document representations"
    - "universal language model fine tuning for text classification"
    - "regularizing and optimizing lstm language models"
    - "norec the norwegian review corpus"

Connected through hyperedge HE-2 (induced by source paper "autonlp nlp feature recommendations for text analytics applications"):
  with members:
    - "bag of tricks for efficient text classification"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "a multilingual encoding method for text classification and dialect identification using convolutional neural network"):
  with members:
    - "which encoding is the best for text classification in chinese english japanese and korean"
    - "very deep convolutional networks for text classification"
    - "bag of tricks for efficient text classification"
    - "character level convolutional networks for text classification"
    - "a critical review of recurrent neural networks for sequence learning"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "deep learning in neural networks"
    - "improved transition based parsing by modeling characters instead of words with lstms"

Connected through hyperedge HE-4 (induced by source paper "latent variable generative models for data efficient text classification"):
  with members:
    - "smaller text classifiers with discriminative cluster embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structured attention networks"
    - "recurrent neural network grammars"
    - "very deep convolutional networks for text classification"
    - "importance weighted autoencoders"
    - "character level convolutional networks for text classification"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-5 (induced by source paper "cnn lstm models for multi speaker source separation using bayesian hyper parameter optimization"):
  with members:
    - "speaker independent speech separation with deep attractor network"
    - "memory time span in lstms for multi speaker source separation"
    - "convolutional vs recurrent neural networks for audio source separation"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-6 (induced by source paper "mimicking human process text representation via latent semantic clustering for classification"):
  with members:
    - "joint embedding of words and labels for text classification"
    - "a structured self attentive sentence embedding"
    - "tensorflow a system for large scale machine learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "using deep learning for title based semantic subject indexing to reach competitive performance to full text"):
  with members:
    - "lstm a search space odyssey"
    - "on the state of the art of evaluation in neural language models"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "do convolutional networks need to be deep for text classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "deep extreme multi label learning"
    - "using titles vs full text as source for automated semantic document annotation"

Connected through hyperedge HE-8 (induced by source paper "seven augmenting word embeddings with unsupervised relation vectors"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "modeling semantic relatedness using global relation vectors"
    - "specialising word vectors for lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "classifying relations via long short term memory networks along shortest dependency path"

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.CL(Computation and Language)