human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi view knowledge graph embedding for entity alignment"
  Abstract: We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.

Connected through hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs"):
  with members:
    - "a survey on knowledge graphs representation acquisition and applications"
    - "knowledge graph embedding for link prediction a comparative analysis"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "jointly learning entity and relation representations for entity alignment"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "aligning cross lingual entities with multi aspect information"

Connected through hyperedge HE-2 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank"):
  with members:
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "pykg2vec a python library for knowledge graph embedding"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "a re evaluation of knowledge graph completion methods"
    - "hypernetwork knowledge graph embeddings"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "recurrent event network for reasoning over temporal knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "dividing the ontology alignment task with semantic embeddings and logic based modules"):
  with members:
    - "a survey of blocking and filtering techniques for entity resolution"
    - "matching entities across different knowledge graphs with graph embeddings"
    - "breaking down the ontology alignment task with a lexical index and neural embeddings"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "knowledge graph alignment network with gated multi hop neighborhood aggregation"):
  with members:
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "multi channel graph neural network for entity alignment"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "enriching medcial terminology knowledge bases via pre trained language model and graph convolutional network"):
  with members:
    - "ernie enhanced language representation with informative entities"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-6 (induced by source paper "collective embedding based entity alignment via adaptive features"):
  with members:
    - "end to end entity resolution for big data a survey"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "multi channel graph neural network for entity alignment"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "predicting completeness in knowledge bases"
    - "cross lingual entity alignment via joint attribute preserving embedding"

Connected through hyperedge HE-7 (induced by source paper "knowledge graph entity alignment with graph convolutional networks lessons learned"):
  with members:
    - "deep graph matching consensus"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "multi channel graph neural network for entity alignment"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "neural message passing for quantum chemistry"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "raka co training of relationships and attributes for cross lingual knowledge alignment"):
  with members:
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "multi channel graph neural network for entity alignment"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "neural machine translation by jointly learning to align and translate"
    - "sigma simple greedy matching for aligning large knowledge bases"
    - "paris probabilistic alignment of relations instances and schema"

Question: Please tell me which class the center node 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)