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: "predicting completeness in knowledge bases"
  Abstract: Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where a knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact prediction.

Connected through hyperedge HE-1 (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"
    - "cross lingual entity alignment via joint attribute preserving embedding"

Connected through hyperedge HE-2 (induced by source paper "embedding cardinality constraints in neural link predictors"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "cardinal virtues extracting relation cardinalities from text"
    - "adversarial sets for regularising neural link predictors"
    - "poincar e embeddings for learning hierarchical representations"
    - "complex embeddings for simple link prediction"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "wikidata from a research perspective a systematic mapping study of wikidata"):
  with members:
    - "using word embeddings for visual data exploration with ontodia and wikidata"
    - "linking imagenet wordnet synsets with wikidata"
    - "guidelines for systematic mapping studies in security engineering"
    - "wikidata vandalism detection the loganberry vandalism detector at wsdm cup 2017"
    - "overview of the wikidata vandalism detection task at wsdm cup 2017"
    - "learning to generate one sentence biographies from wikidata"
    - "building automated vandalism detection tools for wikidata"
    - "wikidata through the eyes of dbpedia"

Connected through hyperedge HE-4 (induced by source paper "discovering implicational knowledge in wikidata"):
  with members:
    - "you the web and your device longitudinal characterization of browsing habits"
    - "on the usability of probably approximately correct implication bases"

Connected through hyperedge HE-5 (induced by source paper "non parametric class completeness estimators for collaborative knowledge graphs the case of wikidata"):
  with members:
    - "growing wikipedia across languages via recommendation"

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.DB(Databases)