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: "truth finding on the deep web is the problem solved"
  Abstract: The amount of useful information available on the Web has been growing at a dramatic pace in recent years and people rely more and more on the Web to fulfill their information needs. In this paper, we study truthfulness of Deep Web data in two domains where we believed data are fairly clean and data quality is important to people's lives: {\em Stock} and {\em Flight}. To our surprise, we observed a large amount of inconsistency on data from different sources and also some sources with quite low accuracy. We further applied on these two data sets state-of-the-art {\em data fusion} methods that aim at resolving conflicts and finding the truth, analyzed their strengths and limitations, and suggested promising research directions. We wish our study can increase awareness of the seriousness of conflicting data on the Web and in turn inspire more research in our community to tackle this problem.

Connected through hyperedge HE-1 (induced by source paper "data market platforms trading data assets to solve data problems vision paper"):
  with members:
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "revenue maximization for query pricing"
    - "a marketplace for data an algorithmic solution"
    - "a survey on truth discovery"
    - "a theory of pricing private data"

Connected through hyperedge HE-2 (induced by source paper "crowdsourced truth discovery in the presence of hierarchies for knowledge fusion"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "from data fusion to knowledge fusion"
    - "a survey on truth discovery"
    - "crowder crowdsourcing entity resolution"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-3 (induced by source paper "ed2 two stage active learning for error detection technical report"):
  with members:
    - "boostclean automated error detection and repair for machine learning"
    - "holoclean holistic data repairs with probabilistic inference"
    - "xgboost a scalable tree boosting system"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "medtruth a semi supervised approach to discovering knowledge condition information from multi source medical data"):
  with members:
    - "crowdsourcing ground truth for medical relation extraction"
    - "does william shakespeare really write hamlet knowledge representation learning with confidence"
    - "a survey on truth discovery"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "when truth discovery meets medical knowledge graph estimating trustworthiness degree for medical knowledge condition"):
  with members:
    - "crowdsourcing ground truth for medical relation extraction"
    - "a survey on truth discovery"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "lptd achieving lightweight and privacy preserving truth discovery in ciot"):
  with members:
    - "cognitive internet of things a new paradigm beyond connection"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"

Connected through hyperedge HE-7 (induced by source paper "holoclean holistic data repairs with probabilistic inference"):
  with members:
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "incremental knowledge base construction using deepdive"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "dimmwitted a study of main memory statistical analytics"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-8 (induced by source paper "truth discovery to resolve object conflicts in linked data"):
  with members:
    - "a survey on truth discovery"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "linked data integration with conflicts"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

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)