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 "a re evaluation of knowledge graph completion methods"):
  Abstract: Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have been published in top conferences in several research fields including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model which can substantially affect the final results. We conduct extensive experiments and report the performance of several existing methods using our protocol.

Includes members:
  Member "rotate knowledge graph embedding by relational rotation in complex space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "learning attention based embeddings for relation prediction in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient rule learning with template saturation for knowledge graph completion")
  Member "tucker tensor factorization for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "on the knowledge graph completion using translation based embedding the loss is as important as the score":
    also connected through other hyperedges:
      (none)
  Member "toward understanding the effect of loss function on then performance of knowledge graph embedding":
    also connected through other hyperedges:
      (none)
  Member "dolores deep contextualized knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kg bert bert for knowledge graph completion")
  Member "a capsule network based embedding model for knowledge graph completion and search personalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "end to end structure aware convolutional networks for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient rule learning with template saturation for knowledge graph completion")

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.CL(Computation and Language)