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: "finding streams in knowledge graphs to support fact checking"
  Abstract: The volume and velocity of information that gets generated online limits current journalistic practices to fact-check claims at the same rate. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. To this end, we present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a (subject, predicate, object) triple. We view a knowledge graph of background information about real-world entities as a flow network, and knowledge as a fluid, abstract commodity. We show that computational fact checking of such a triple then amounts to finding a "knowledge stream" that emanates from the subject node and flows toward the object node through paths connecting them. Evaluation on a range of real-world and hand-crafted datasets of facts related to entertainment, business, sports, geography and more reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful path patterns and surface relevant facts that may help a human fact checker corroborate or refute a claim.

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 "benchmarking knowledge graphs on the web"):
  with members:
    - "bengal an automatic benchmark generator for entity recognition and linking"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "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-3 (induced by source paper "learning from fact checkers analysis and generation of fact checking language"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "variational memory encoder decoder"
    - "fact checking in community forums"
    - "automated hate speech detection and the problem of offensive language"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-4 (induced by source paper "fakta an automatic end to end fact checking system"):
  with members:
    - "hunting for troll comments in news community forums"
    - "automatic stance detection using end to end memory networks"
    - "predicting factuality of reporting and bias of news media sources"
    - "fever a large scale dataset for fact extraction and verification"
    - "fact checking in community forums"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "fully automated fact checking using external sources"

Connected through hyperedge HE-5 (induced by source paper "studying fake news via network analysis detection and mitigation"):
  with members:
    - "fake news detection on social media a data mining perspective"
    - "some like it hoax automated fake news detection in social networks"
    - "computational fact checking from knowledge networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "characterizing the life cycle of online news stories using social media reactions"

Connected through hyperedge HE-6 (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"
    - "computing lpmln using asp and mln solvers"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-7 (induced by source paper "data exploration and validation on dense knowledge graphs for biomedical research"):
  with members:
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-8 (induced by source paper "integrating stance detection and fact checking in a unified corpus"):
  with members:
    - "in search of credible news"
    - "automatic stance detection using end to end memory networks"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "fully automated fact checking using external sources"
    - "a decomposable attention model for natural language inference"
    - "squad 100 000 questions for machine comprehension of text"
    - "rationalizing neural predictions"

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)