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: "graph pattern entity ranking model for knowledge graph completion"
  Abstract: Knowledge graphs have evolved rapidly in recent years and their usefulness has been demonstrated in many artificial intelligence tasks. However, knowledge graphs often have lots of missing facts. To solve this problem, many knowledge graph embedding models have been developed to populate knowledge graphs and these have shown outstanding performance. However, knowledge graph embedding models are so-called black boxes, and the user does not know how the information in a knowledge graph is processed and the models can be difficult to interpret. In this paper, we utilize graph patterns in a knowledge graph to overcome such problems. Our proposed model, the {\it graph pattern entity ranking model} (GRank), constructs an entity ranking system for each graph pattern and evaluates them using a ranking measure. By doing so, we can find graph patterns which are useful for predicting facts. Then, we perform link prediction tasks on standard datasets to evaluate our GRank method. We show that our approach outperforms other state-of-the-art approaches such as ComplEx and TorusE for standard metrics such as HITS@{\it n} and MRR. Moreover, our model is easily interpretable because the output facts are described by graph patterns.

Connected through hyperedge HE-1 (induced by source paper "towards combinational relation linking over knowledge graphs"):
  with members:
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "toruse knowledge graph embedding on a lie group"
    - "explainable entity based recommendations with knowledge graphs"
    - "modeling relation paths for representation learning of knowledge bases"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-2 (induced by source paper "combination of unified embedding model and observed features for knowledge graph completion"):
  with members:
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "toruse knowledge graph embedding on a lie group"
    - "semi supervised classification with graph convolutional networks"
    - "question answering with subgraph embeddings"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "combination of embedding models and collaboration with observed feature models for knowledge graph completion"):
  with members:
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "toruse knowledge graph embedding on a lie group"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"
    - "question answering with subgraph embeddings"

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)