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: "human powered sorts and joins"
  Abstract: Crowdsourcing markets like Amazon's Mechanical Turk (MTurk) make it possible to task people with small jobs, such as labeling images or looking up phone numbers, via a programmatic interface. MTurk tasks for processing datasets with humans are currently designed with significant reimplementation of common workflows and ad-hoc selection of parameters such as price to pay per task. We describe how we have integrated crowds into a declarative workflow engine called Qurk to reduce the burden on workflow designers. In this paper, we focus on how to use humans to compare items for sorting and joining data, two of the most common operations in DBMSs. We describe our basic query interface and the user interface of the tasks we post to MTurk. We also propose a number of optimizations, including task batching, replacing pairwise comparisons with numerical ratings, and pre-filtering tables before joining them, which dramatically reduce the overall cost of running sorts and joins on the crowd. In an experiment joining two sets of images, we reduce the overall cost from $67 in a naive implementation to about $3, without substantially affecting accuracy or latency. In an end-to-end experiment, we reduced cost by a factor of 14.5.

Connected through hyperedge HE-1 (induced by source paper "reducing uncertainty of schema matching via crowdsourcing with accuracy rates"):
  with members:
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "mining frequent itemsets over uncertain databases"
    - "active learning for crowd sourced databases"
    - "human assisted graph search it s okay to ask questions"

Connected through hyperedge HE-2 (induced by source paper "revealing the basis ordinal embedding through geometry"):
  with members:
    - "metric and kernel learning using a linear transformation"
    - "noisy sorting without resampling"

Connected through hyperedge HE-3 (induced by source paper "select your questions wisely for entity resolution with crowd errors"):
  with members:
    - "leveraging transitive relations for crowdsourced joins"
    - "evaluating the crowd with confidence"
    - "on the complexity of mining itemsets from the crowd using taxonomies"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "whom to ask jury selection for decision making tasks on micro blog services"
    - "human assisted graph search it s okay to ask questions"

Connected through hyperedge HE-4 (induced by source paper "big data analytics using cloud and crowd"):
  with members:
    - "an iterative method for calculating robust rating scores"
    - "a trust based recruitment framework for multi hop social participatory sensing"
    - "crowder crowdsourcing entity resolution"
    - "serf and turf crowdturfing for fun and profit"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-5 (induced by source paper "clamshell speeding up crowds for low latency data labeling"):
  with members:
    - "mllib machine learning in apache spark"
    - "finish them pricing algorithms for human computation"
    - "crowder crowdsourcing entity resolution"
    - "analytic methods for optimizing realtime crowdsourcing"
    - "active learning with statistical models"

Connected through hyperedge HE-6 (induced by source paper "crowdgather entity extraction over structured domains"):
  with members:
    - "optimal algorithms for crawling a hidden database in the web"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-7 (induced by source paper "probably approximately optimal query optimization"):
  with members:
    - "exploiting correlations for expensive predicate evaluation"

Connected through hyperedge HE-8 (induced by source paper "finish them pricing algorithms for human computation"):
  with members:
    - "evaluating the crowd with confidence"
    - "crowdsourcing control moving beyond multiple choice"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "human assisted graph search it s okay to ask questions"
    - "budget optimal task allocation for reliable crowdsourcing systems"

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)