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 "knowledge graph alignment network with gated multi hop neighborhood aggregation"):
  Abstract: Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.

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 "rotate knowledge graph embedding by relational rotation in complex space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "learning to exploit long term relational dependencies in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "deep graph matching consensus")
  Member "relation aware entity alignment for heterogeneous knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep graph matching consensus")
  Member "graph matching networks for learning the similarity of graph structured objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep graph matching consensus")

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)