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: "robust cross lingual hypernymy detection using dependency context"
  Abstract: Cross-lingual Hypernymy Detection involves determining if a word in one language ("fruit") is a hypernym of a word in another language ("pomme" i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BISPARSE-DEP can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages -- Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.

Connected through hyperedge HE-1 (induced by source paper "improving cross lingual word embeddings by meeting in the middle"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "unsupervised machine translation using monolingual corpora only"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-2 (induced by source paper "specialising word vectors for lexical entailment"):
  with members:
    - "cross lingual induction and transfer of verb classes based on word vector space specialisation"
    - "a vector space for distributional semantics for entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "neural belief tracker data driven dialogue state tracking"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "a large annotated corpus for learning natural language inference"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "word representations via gaussian embedding"

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)