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 knowledge graph alignment via graph matching neural network"
  Abstract: Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we introduce the topic entity graph, a local sub-graph of an entity, to represent entities with their contextual information in KG. From this view, the KB-alignment task can be formulated as a graph matching problem; and we further propose a graph-attention based solution, which first matches all entities in two topic entity graphs, and then jointly model the local matching information to derive a graph-level matching vector. Experiments show that our model outperforms previous state-of-the-art methods by a large margin.

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"
    - "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"
    - "gromov wasserstein learning for graph matching and node embedding"

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"
    - "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-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"
    - "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-4 (induced by source paper "coordinated reasoning for cross lingual knowledge graph alignment"):
  with members:
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "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 "knowledge graph alignment network with gated multi hop neighborhood aggregation"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "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-6 (induced by source paper "engn a high throughput and energy efficient accelerator for large graph neural networks"):
  with members:
    - "bandwidth reduction using importance weighted pruning on ring allreduce"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"
    - "recent advances in recurrent neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

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"
    - "multi view knowledge graph embedding for entity alignment"
    - "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:
    - "multi view knowledge graph embedding for entity alignment"
    - "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.LG(Machine Learning)