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: "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
  Abstract: Automatic evaluation of semantic rationality is an important yet challenging task, and current automatic techniques cannot well identify whether a sentence is semantically rational. The methods based on the language model do not measure the sentence by rationality but by commonness. The methods based on the similarity with human written sentences will fail if human-written references are not available. In this paper, we propose a novel model called Sememe-Word-Matching Neural Network (SWM-NN) to tackle semantic rationality evaluation by taking advantage of sememe knowledge base HowNet. The advantage is that our model can utilize a proper combination of sememes to represent the fine-grained semantic meanings of a word within the specific contexts. We use the fine-grained semantic representation to help the model learn the semantic dependency among words. To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset. Experimental results on this dataset show that the proposed model outperforms the competitive baselines with a 5.4\% improvement in accuracy.

Connected through hyperedge HE-1 (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"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "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-2 (induced by source paper "openhownet an open sememe based lexical knowledge base"):
  with members:
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "primal meaning recommendation via on line encyclopedia"):
  with members:
    - "exploration on generating traditional chinese medicine prescription from symptoms with an end to end method"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "skip thought vectors"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"

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)