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: "discovering implicational knowledge in wikidata"
  Abstract: Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Among the freely available knowledge graphs, Wikidata stands out by being collaboratively edited and curated. Among the vast numbers of facts, complex knowledge is just waiting to be discovered, but the sheer size of Wikidata makes this infeasible for human editors. We apply Formal Concept Analysis to efficiently identify and succinctly represent comprehensible implications that are implicitly present in the data. As a first step, we describe a systematic process to extract conceptual knowledge from Wikidata’s complex data model, thus providing a method for obtaining large real-world data sets for FCA. We conduct experiments that show the principal feasibility of the approach, yet also illuminate some of the limitations, and give examples of interesting knowledge discovered.

Connected through hyperedge HE-1 (induced by source paper "knowledge cores in large formal contexts"):
  with members:
    - "relevant attributes in formal contexts"

Connected through hyperedge HE-2 (induced by source paper "fca2vec embedding techniques for formal concept analysis"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "word2vec parameter learning explained"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "orometric methods in bounded metric data"):
  with members:
    - "smote synthetic minority over sampling technique"

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.AI(Artificial Intelligence)