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 "multi channel graph neural network for entity alignment"):
  Abstract: Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. Extensive experiments on five publicly available datasets demonstrate our superior performance (5% Hits@1 up on average).

Includes members:
  Member "unifying knowledge graph learning and recommendation towards a better understanding of user preferences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "neural collective entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences")
  Member "joint representation learning of cross lingual words and entities via attentive distant supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "is aligning embedding spaces a challenging task an analysis of the existing methods")
  Member "explainable reasoning over knowledge graphs for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on knowledge graph based recommender systems")
  Member "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  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 "paris probabilistic alignment of relations instances and schema":
    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.CL(Computation and Language)