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: "openhownet an open sememe based lexical knowledge base"
  Abstract: In this paper, we present an open sememe-based lexical knowledge base OpenHowNet. Based on well-known HowNet, OpenHowNet comprises three components: core data which is composed of more than 100 thousand senses annotated with sememes, OpenHowNet Web which gives a brief introduction to OpenHowNet as well as provides online exhibition of OpenHowNet information, and OpenHowNet API which includes several useful APIs such as accessing OpenHowNet core data and drawing sememe tree structures of senses. In the main text, we first give some backgrounds including definition of sememe and details of HowNet. And then we introduce some previous HowNet and sememe-based research works. Last but not least, we detail the constituents of OpenHowNet and their basic features and functionalities. Additionally, we briefly make a summary and list some future works.

Connected through hyperedge HE-1 (induced by source paper "multi channel reverse dictionary model"):
  with members:
    - "enhancing recurrent neural networks with sememes"
    - "modeling semantic compositionality with sememe knowledge"
    - "open the boxes of words incorporating sememes into textual adversarial attack"
    - "using multi sense vector embeddings for reverse dictionaries"
    - "incorporating glosses into neural word sense disambiguation"
    - "building a knowledge graph from natural language definitions for interpretable text entailment recognition"
    - "learning to compute word embeddings on the fly"
    - "mapping text to knowledge graph entities using multi sense lstms"

Connected through hyperedge HE-2 (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"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "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-3 (induced by source paper "textual adversarial attack as combinatorial optimization"):
  with members:
    - "modeling semantic compositionality with sememe knowledge"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "language modeling with sparse product of sememe experts"
    - "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-4 (induced by source paper "open the boxes of words incorporating sememes into textual adversarial attack"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "language modeling with sparse product of sememe experts"
    - "generating natural language adversarial examples"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "generating natural adversarial examples"
    - "intriguing properties of neural networks"

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)