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 "collective embedding based entity alignment via adaptive features"):
  Abstract: Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current embedding-based solutions treat entities independently and fail to take into account the interdependence between entities. In addition, most of embedding-based EA methods either fuse different features on representation-level and generate unified entity embedding for alignment, which potentially causes information loss, or aggregate features on outcome-level with hand-tuned weights, which is not practical with increasing numbers of features. To tackle these deficiencies, we propose a collective embedding-based EA framework with adaptive feature fusion mechanism. We first employ three representative features, i.e., structural, semantic and string signals, for capturing different aspects of the similarity between entities in heterogeneous KGs. These features are then integrated at outcome-level, with dynamically assigned weights generated by our carefully devised adaptive feature fusion strategy. Eventually, in order to make collective EA decisions, we formulate EA as the classical stable matching problem between entities to be aligned, with preference lists constructed using fused feature matrix. It is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority. We also perform ablation study to gain insights into framework modules.

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 "end to end entity resolution for big data a survey":
    also connected through other hyperedges:
      (none)
  Member "unifying knowledge graph learning and recommendation towards a better understanding of user preferences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "learning to exploit long term relational dependencies in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "multi channel graph neural network for entity alignment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep graph matching consensus")
  Member "relation aware entity alignment for heterogeneous knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "predicting completeness in knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "embedding cardinality constraints in neural link predictors")

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)