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: "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
  Abstract: Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

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"
    - "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-2 (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"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "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-3 (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-4 (induced by source paper "a survey on knowledge graph embeddings with literals which model links better literal ly"):
  with members:
    - "incorporating literals into knowledge graph embeddings"
    - "embedding multimodal relational data for knowledge base completion"
    - "a first experiment on including text literals in kglove"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "multi task neural network for non discrete attribute prediction in knowledge graphs"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-5 (induced by source paper "aligning cross lingual entities with multi aspect information"):
  with members:
    - "matching entities across different knowledge graphs with graph embeddings"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "simple attention based representation learning for ranking short social media posts"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "machine translation using semantic web technologies a survey"

Connected through hyperedge HE-6 (induced by source paper "jointly learning entity and relation representations for entity alignment"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "semi supervised user geolocation via graph convolutional networks"
    - "adversarial training for multi context joint entity and relation extraction"
    - "variational reasoning for question answering with knowledge graph"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"

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"
    - "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-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"
    - "predicting completeness in knowledge bases"
    - "cross lingual entity alignment via joint attribute preserving embedding"

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)