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: "cross lingual entity alignment via joint attribute preserving embedding"
  Abstract: Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"

Connected through hyperedge HE-2 (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"
    - "multi view knowledge graph embedding for entity alignment"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "coordinated reasoning for cross lingual knowledge graph alignment"):
  with members:
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "question answering on freebase via relation extraction and textual evidence"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning to exploit long term relational dependencies in knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "representation learning on graphs with jumping knowledge networks"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-6 (induced by source paper "knowledge graph alignment network with gated multi hop neighborhood aggregation"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "enriching medcial terminology knowledge bases via pre trained language model and graph convolutional network"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "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"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-8 (induced by source paper "collective embedding based entity alignment via adaptive features"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "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"

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.CL(Computation and Language)