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 "towards combinational relation linking over knowledge graphs"):
  Abstract: Given a natural language phrase, relation linking aims to find a relation (predicate or property) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to a more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. mother-in-law). In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. We also introduce external knowledge to enhance the system understanding ability. Finally, we conduct extensive experiments over the real knowledge graph to study the performance of the proposed method.

Includes members:
  Member "graph pattern entity ranking model for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combination of unified embedding model and observed features for knowledge graph completion")
  Member "earl joint entity and relation linking for question answering over knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge based conversational search")
  Member "question answering on knowledge bases and text using universal schema and memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning cross context entity representations from text")
  Member "joint extraction of entities and relations based on a novel tagging scheme":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "toruse knowledge graph embedding on a lie group":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "explainable entity based recommendations with knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "modeling relation paths for representation learning of knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "from one point to a manifold knowledge graph embedding for precise link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an evaluation of knowledge graph embeddings for autonomous driving data experience and practice")

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)