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 "interpreting embedding models of knowledge bases a pedagogical approach"):
  Abstract: Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.

Includes members:
  Member "inducing interpretability in knowledge graph embeddings":
    also connected through other hyperedges:
      (none)
  Member "an interpretable knowledge transfer model for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion")
  Member "an overview of embedding models of entities and relationships for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "what does explainable ai really mean a new conceptualization of perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "directions for explainable knowledge enabled systems")
  Member "analogical inference for multi relational embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "neighborhood mixture model for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relational memory based knowledge graph embedding")
  Member "ksr a semantic representation of knowledge graph within a novel unsupervised paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inducing interpretability in knowledge graph embeddings")
  Member "the mythos of model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")

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)