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: "discriminative predicate path mining for fact checking in knowledge graphs"
  Abstract: Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions. Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S . We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "scrutinizer a mixed initiative approach to large scale data driven claim verification"):
  with members:
    - "explainable fact checking with probabilistic answer set programming"
    - "claimrank detecting check worthy claims in arabic and english"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "automated fact checking task formulations methods and future directions"
    - "fact checking in community forums"
    - "the rise of social bots"
    - "computational fact checking from knowledge networks"
    - "believe it or not adding belief annotations to databases"

Connected through hyperedge HE-3 (induced by source paper "benchmarking knowledge graphs on the web"):
  with members:
    - "finding streams in knowledge graphs to support fact checking"
    - "bengal an automatic benchmark generator for entity recognition and linking"
    - "analysis of named entity recognition and linking for tweets"
    - "thousands of debitcredit transactions per second easy and inexpensive"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "fake news early detection a theory driven model"):
  with members:
    - "xfake explainable fake news detector with visualizations"
    - "open issues in combating fake news interpretability as an opportunity"
    - "fake news detection on social media using geometric deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "a survey on natural language processing for fake news detection"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"

Connected through hyperedge HE-5 (induced by source paper "network based fake news detection a pattern driven approach"):
  with members:
    - "fake news early detection a theory driven model"
    - "learning hierarchical discourse level structure for fake news detection"
    - "fake news a survey of research detection methods and opportunities"
    - "a survey on natural language processing for fake news detection"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "the spread of low credibility content by social bots"
    - "ti cnn convolutional neural networks for fake news detection"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"

Connected through hyperedge HE-6 (induced by source paper "credibility based fake news detection"):
  with members:
    - "fake news early detection a theory driven model"
    - "learning hierarchical discourse level structure for fake news detection"
    - "network based fake news detection a pattern driven approach"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "predicting factuality of reporting and bias of news media sources"
    - "fake news a survey of research detection methods and opportunities"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"

Connected through hyperedge HE-7 (induced by source paper "representation learning in heterogeneous professional social networks with ambiguous social connections"):
  with members:
    - "attributed social network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "representation learning on graphs methods and applications"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "proje embedding projection for knowledge graph completion"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-8 (induced by source paper "explainable fact checking with probabilistic answer set programming"):
  with members:
    - "claimrank detecting check worthy claims in arabic and english"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "finding streams in knowledge graphs to support fact checking"
    - "computing lpmln using asp and mln solvers"
    - "computational fact checking from knowledge networks"

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)