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 "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank"):
  Abstract: In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the current experimental setting, multiple different scores are employed to assess different aspects of model performance. We analyze the informative value of these evaluation measures and identify several shortcomings. In particular, we demonstrate that all existing scores can hardly be used to compare results across different datasets. Moreover, this problem may also arise when comparing different train/test splits for the same dataset. We show that this leads to various problems in the interpretation of results, which may support misleading conclusions. Therefore, we propose a different evaluation and demonstrate empirically how this helps for fair, comparable and interpretable assessment of model performance.

Includes members:
  Member "multi view knowledge graph embedding for entity alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "knowledge graph entity alignment with graph convolutional networks lessons learned":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active learning for entity alignment")
  Member "cross lingual knowledge graph alignment via graph matching neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep graph matching consensus")
  Member "pykg2vec a python library for knowledge graph embedding":
    also connected through other hyperedges:
      (none)
  Member "knowledge graph alignment network with gated multi hop neighborhood aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active learning for entity alignment")
  Member "a re evaluation of knowledge graph completion methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interacte improving convolution based knowledge graph embeddings by increasing feature interactions")
  Member "hypernetwork knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "learning to exploit long term relational dependencies in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")

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.LG(Machine Learning)