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 "knowledge graph error detection and completion"):
  Abstract: In the era of big data, people face enormous challenges in acquiring information and knowledge. A knowledge graph (KG) lays the foundation for the knowledge-based organization and intelligent application in the Internet age with its powerful semantic processing capabilities and open organization capabilities. In recent years, the research and applications of large-scale knowledge graph libraries have attracted increasing attention in academic and industrial circles. The knowledge graph aims to describe the various entities or concepts and their relationships existing in the objective world, which constitutes a huge semantic network map. It usually stores knowledge in the form of triples (head entity, relationship, tail entity), which can be simplified to $(h, r, t)$.

Includes members:
  Member "does william shakespeare really write hamlet knowledge representation learning with confidence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "error detection in knowledge graphs path ranking embeddings or both")
  Member "discriminative predicate path mining for fact checking in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graphs")
  Member "compositional learning of relation paths embedding for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recurrent one hop predictions for reasoning over knowledge graphs")
  Member "modeling relation paths for representation learning of knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "embedding entities and relations for learning and inference in knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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.AI(Artificial Intelligence)