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: "incorporating chinese characters of words for lexical sememe prediction"
  Abstract: Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words. To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words. We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words.

Connected through hyperedge HE-1 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence"):
  with members:
    - "incorporating glosses into neural word sense disambiguation"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "implementing a reverse dictionary based on word definitions using a node graph architecture"
    - "neural machine translation of rare words with subword units"
    - "learning to understand phrases by embedding the dictionary"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "enhancing recurrent neural networks with sememes"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "towards a robust deep neural network in text domain a survey"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "language modeling with sparse product of sememe experts"
    - "generating natural language adversarial examples"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "extending neural generative conversational model using external knowledge sources"

Connected through hyperedge HE-3 (induced by source paper "towards building a multilingual sememe knowledge base predicting sememes for babelnet synsets"):
  with members:
    - "multi channel reverse dictionary model"
    - "enhancing recurrent neural networks with sememes"
    - "modeling semantic compositionality with sememe knowledge"
    - "open the boxes of words incorporating sememes into textual adversarial attack"
    - "openhownet an open sememe based lexical knowledge base"
    - "language modeling with sparse product of sememe experts"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-4 (induced by source paper "modeling semantic compositionality with sememe knowledge"):
  with members:
    - "cos960 a chinese word similarity dataset of 960 word pairs"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
    - "improving sparse word representations with distributional inference for semantic composition"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-5 (induced by source paper "openhownet an open sememe based lexical knowledge base"):
  with members:
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "language modeling with sparse product of sememe experts"):
  with members:
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "regularizing and optimizing lstm language models"
    - "revisiting activation regularization for language rnns"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural headline generation with minimum risk training"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "improving neural language models with a continuous cache"
    - "using the output embedding to improve language models"

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)