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: "language modeling with sparse product of sememe experts"
  Abstract: Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but not necessarily atomic semantic units. Inspired by HowNet, we use sememes, the minimum semantic units in human languages, to represent the implicit semantics behind words for language modeling, named Sememe-Driven Language Model (SDLM). More specifically, to predict the next word, SDLM first estimates the sememe distribution gave textual context. Afterward, it regards each sememe as a distinct semantic expert, and these experts jointly identify the most probable senses and the corresponding word. In this way, SDLM enables language models to work beyond word-level manipulation to fine-grained sememe-level semantics and offers us more powerful tools to fine-tune language models and improve the interpretability as well as the robustness of language models. Experiments on language modeling and the downstream application of headline gener- ation demonstrate the significant effect of SDLM. Source code and data used in the experiments can be accessed at https:// github.com/thunlp/SDLM-pytorch.

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"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "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"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "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 "knowledge augmented language model and its application to unsupervised named entity recognition"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "deep contextualized word representations"
    - "building language models for text with named entities"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence tagging with bidirectional language models"
    - "regularizing and optimizing lstm language models"
    - "a neural knowledge language model"
    - "reference aware language models"

Connected through hyperedge HE-4 (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"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-5 (induced by source paper "textual adversarial attack as combinatorial optimization"):
  with members:
    - "modeling semantic compositionality with sememe knowledge"
    - "openhownet an open sememe based lexical knowledge base"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "generating natural language adversarial examples"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "hotflip white box adversarial examples for text classification"
    - "generating natural adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "modeling semantic compositionality with sememe knowledge"):
  with members:
    - "cos960 a chinese word similarity dataset of 960 word pairs"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "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-7 (induced by source paper "open the boxes of words incorporating sememes into textual adversarial attack"):
  with members:
    - "openhownet an open sememe based lexical knowledge base"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "generating natural language adversarial examples"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "generating natural adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-8 (induced by source paper "openhownet an open sememe based lexical knowledge base"):
  with members:
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "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"

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)