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: "leveraging transitive relations for crowdsourced joins"
  Abstract: The development of crowdsourced query processing systems has recently attracted a significant attention in the database community. A variety of crowdsourced queries have been investigated. In this paper, we focus on the crowdsourced join query which aims to utilize humans to find all pairs of matching objects from two collections. As a human-only solution is expensive, we adopt a hybrid human-machine approach which first uses machines to generate a candidate set of matching pairs, and then asks humans to label the pairs in the candidate set as either matching or non-matching. Given the candidate pairs, existing approaches will publish all pairs for verification to a crowdsourcing platform. However, they neglect the fact that the pairs satisfy transitive relations. As an example, if $o_1$ matches with $o_2$, and $o_2$ matches with $o_3$, then we can deduce that $o_1$ matches with $o_3$ without needing to crowdsource $(o_1, o_3)$. To this end, we study how to leverage transitive relations for crowdsourced joins. We propose a hybrid transitive-relations and crowdsourcing labeling framework which aims to crowdsource the minimum number of pairs to label all the candidate pairs. We prove the optimal labeling order in an ideal setting and propose a heuristic labeling order in practice. We devise a parallel labeling algorithm to efficiently crowdsource the pairs following the order. We evaluate our approaches in both simulated environment and a real crowdsourcing platform. Experimental results show that our approaches with transitive relations can save much more money and time than existing methods, with a little loss in the result quality.

Connected through hyperedge HE-1 (induced by source paper "crowdsourced collective entity resolution with relational match propagation"):
  with members:
    - "minoaner schema agnostic non iterative massively parallel resolution of web entities"
    - "principled graph matching algorithms for integrating multiple data sources"
    - "lazier than lazy greedy"
    - "sigma simple greedy matching for aligning large knowledge bases"
    - "crowder crowdsourcing entity resolution"
    - "large scale collective entity matching"
    - "paris probabilistic alignment of relations instances and schema"

Connected through hyperedge HE-2 (induced by source paper "same cluster querying for overlapping clusters"):
  with members:
    - "community recovery in hypergraphs"
    - "predicting positive and negative links with noisy queries theory practice"
    - "a theoretical analysis of first heuristics of crowdsourced entity resolution"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "technical report optimizing human involvement for entity matching and consolidation"):
  with members:
    - "entity consolidation the golden record problem"
    - "synthesizing mapping relationships using table corpus"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-4 (induced by source paper "schema agnostic progressive entity resolution"):
  with members:
    - "sigma simple greedy matching for aligning large knowledge bases"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-5 (induced by source paper "crowd powered data mining"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "multi label annotation aggregation in crowdsourcing"
    - "clustering via crowdsourcing"
    - "clamshell speeding up crowds for low latency data labeling"
    - "estimating the impact of unknown unknowns on aggregate query results"
    - "comprehensive and reliable crowd assessment algorithms"
    - "finish them pricing algorithms for human computation"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-6 (induced by source paper "comparison based learning from weak oracles"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "learning combinatorial functions from pairwise comparisons"
    - "crowder crowdsourcing entity resolution"
    - "comparison based learning with rank nets"
    - "adaptively learning the crowd kernel"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "approximate nearest neighbor search for low dimensional queries"
    - "hierarchical routing over dynamic wireless networks"

Connected through hyperedge HE-7 (induced by source paper "quality assured synchronized task assignment in crowdsourcing"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-8 (induced by source paper "crowdfusion a crowdsourced approach on data fusion refinement"):
  with members:
    - "fusing data with correlations"
    - "from data fusion to knowledge fusion"
    - "a survey on truth discovery"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "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)