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: "fusing data with correlations"
  Abstract: Many applications rely on Web data and extraction systems to accomplish knowledge-driven tasks. Web information is not curated, so many sources provide inaccurate, or conflicting information. Moreover, extraction systems introduce additional noise to the data. We wish to automatically distinguish correct data and erroneous data for creating a cleaner set of integrated data. Previous work has shown that a na\"ive voting strategy that trusts data provided by the majority or at least a certain number of sources may not work well in the presence of copying between the sources. However, correlation between sources can be much broader than copying: sources may provide data from complementary domains (\emph{negative correlation}), extractors may focus on different types of information (\emph{negative correlation}), and extractors may apply common rules in extraction (\emph{positive correlation, without copying}). In this paper we present novel techniques modeling correlations between sources and applying it in truth finding.

Connected through hyperedge HE-1 (induced by source paper "goggles automatic image labeling with affinity coding"):
  with members:
    - "a closer look at few shot classification"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-2 (induced by source paper "goggles automatic training data generation with affinity coding"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "amic an adaptive information theoretic method to identify multi scale temporal correlations in big time series data accepted version"):
  with members:
    - "data polygamy the many many relationships among urban spatio temporal data sets"
    - "a mutual information approach to calculating nonlinearity"

Connected through hyperedge HE-4 (induced by source paper "snorkel rapid training data creation with weak supervision"):
  with members:
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "fonduer knowledge base construction from richly formatted data"
    - "training classifiers with natural language explanations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "inferring generative model structure with static analysis"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning the structure of generative models without labeled data"
    - "label free supervision of neural networks with physics and domain knowledge"

Connected through hyperedge HE-5 (induced by source paper "crowdfusion a crowdsourced approach on data fusion refinement"):
  with members:
    - "from data fusion to knowledge fusion"
    - "a survey on truth discovery"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"
    - "active learning for crowd sourced databases"

Connected through hyperedge HE-6 (induced by source paper "discovering multiple truths with a hybrid model"):
  with members:
    - "a survey on truth discovery"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-7 (induced by source paper "smartmtd a graph based approach for effective multi truth discovery"):
  with members:
    - "truth discovery algorithms an experimental evaluation"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-8 (induced by source paper "data polygamy the many many relationships among urban spatio temporal data sets"):
  with members:
    - "measuring distance between reeb graphs"

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)