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 "specialising word vectors for lexical entailment"):
  Abstract: We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the is-a or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed model.

Includes members:
  Member "robust cross lingual hypernymy detection using dependency context":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving cross lingual word embeddings by meeting in the middle")
  Member "cross lingual induction and transfer of verb classes based on word vector space specialisation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "a vector space for distributional semantics for entailment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "context mover s distance barycenters optimal transport of contexts for building representations")
  Member "hypernyms under siege linguistically motivated artillery for hypernymy detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the polar framework polar opposites enable interpretability of pre trained word embeddings")
  Member "neural belief tracker data driven dialogue state tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "improving hypernymy detection with an integrated path based and distributional method":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "a large annotated corpus for learning natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextual lensing of universal sentence representations")
  Member "simlex 999 evaluating semantic models with genuine similarity estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)