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 "scef a support confidence aware embedding framework for knowledge graph refinement"):
  Abstract: Knowledge graph (KG) refinement mainly aims at KG completion and correction (i.e., error detection). However, most conventional KG embedding models only focus on KG completion with an unreasonable assumption that all facts in KG hold without noises, ignoring error detection which also should be significant and essential for KG refinement.In this paper, we propose a novel support-confidence-aware KG embedding framework (SCEF), which implements KG completion and correction simultaneously by learning knowledge representations with both triple support and triple confidence. Specifically, we build model energy function by incorporating conventional translation-based model with support and confidence. To make our triple support-confidence more sufficient and robust, we not only consider the internal structural information in KG, studying the approximate relation entailment as triple confidence constraints, but also the external textual evidence, proposing two kinds of triple supports with entity types and descriptions respectively.Through extensive experiments on real-world datasets, we demonstrate SCEF's effectiveness.

Includes members:
  Member "improving knowledge graph embedding using simple constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")
  Member "knowledge graph embedding with iterative guidance from soft rules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graphs")
  Member "does william shakespeare really write hamlet knowledge representation learning with confidence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "error detection in knowledge graphs path ranking embeddings or both")
  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 "convolutional 2d knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graphs")
  Member "ssp semantic space projection for knowledge graph embedding with text descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification")
  Member "proje embedding projection for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "complex embeddings for simple link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")

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)