human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "modeling semantic compositionality with sememe knowledge"):
  Abstract: Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememeincorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC. All the code and data of this paper can be obtained on this https URL.

Includes members:
  Member "cos960 a chinese word similarity dataset of 960 word pairs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "incorporating chinese characters of words for lexical sememe prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence")
  Member "language modeling with sparse product of sememe experts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence")
  Member "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence")
  Member "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "openhownet an open sememe based lexical knowledge base")
  Member "improving sparse word representations with distributional inference for semantic composition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal and aspectual entailment")
  Member "aligning packed dependency trees a theory of composition for distributional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "temporal and aspectual entailment")
  Member "multi step regression learning for compositional distributional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator")

Question: Please tell me which class the source paper 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)