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: "from data fusion to knowledge fusion"
  Abstract: The task of {\em data fusion} is to identify the true values of data items (eg, the true date of birth for {\em Tom Cruise}) among multiple observed values drawn from different sources (eg, Web sites) of varying (and unknown) reliability. A recent survey\cite{LDL+12} has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: {\em knowledge fusion}. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.

Connected through hyperedge HE-1 (induced by source paper "knowledge federation hierarchy and unification"):
  with members:
    - "federated machine learning concept and applications"
    - "cryptonn training neural networks over encrypted data"
    - "federated learning strategies for improving communication efficiency"
    - "entity resolution and federated learning get a federated resolution"
    - "secure federated transfer learning"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "a survey on computational politics"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"

Connected through hyperedge HE-3 (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"
    - "truth finding on the deep web is the problem solved"
    - "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-4 (induced by source paper "action centered information retrieval"):
  with members:
    - "corealmlib an alm library translated from the component library"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "diagnostic reasoning with a prolog"

Connected through hyperedge HE-5 (induced by source paper "ceres distantly supervised relation extraction from the semi structured web"):
  with members:
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "semi supervised web wrapper repair via recursive tree matching"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "automatic wrappers for large scale web extraction"

Connected through hyperedge HE-6 (induced by source paper "enriching knowledge bases with counting quantifiers"):
  with members:
    - "cardinal virtues extracting relation cardinalities from text"
    - "neural architectures for named entity recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "distilling information reliability and source trustworthiness from digital traces"):
  with members:
    - "uncovering the dynamics of crowdlearning and the value of knowledge"
    - "multistage campaigning in social networks"
    - "assessing human error against a benchmark of perfection"
    - "smart broadcasting do you want to be seen"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "learning and forecasting opinion dynamics in social networks"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"

Connected through hyperedge HE-8 (induced by source paper "crowdfusion a crowdsourced approach on data fusion refinement"):
  with members:
    - "fusing data with correlations"
    - "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"

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)