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 "raka co training of relationships and attributes for cross lingual knowledge alignment"):
  Abstract: Cross-lingual knowledge alignment suffers from the attribute heterogeneity when leveraging the attributes and also suffers from the conflicts when combing the results inferred from attributes and relationships. This paper proposes an interaction based attribute model to capture the attribute-level interactions for estimating entity similarities, eliminating the negative impact of the dissimilar attributes. A matrix-based strategy is adopted in the model to accelerate the similarity estimation. We further propose a co-training framework together with three merge strategies to combine the alignments inferred by the attribute model and the relationship model. The whole framework can effectively and efficiently infer the aligned entities, relationships, attributes, and values simultaneously. Experimental results on several cross-lingual knowledge datasets show that our model significantly outperforms the state-of-the-art comparison methods (improving 2.35-51.57% in terms of Hit Ratio@1).

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 "cross lingual knowledge graph alignment via graph matching neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "multi channel graph neural network for entity alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "cross lingual entity alignment via joint attribute preserving embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "multilingual knowledge graph embeddings for cross lingual knowledge 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 "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "sigma simple greedy matching for aligning large knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.CL(Computation and Language)