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: "evaluating the crowd with confidence"
  Abstract: Worker quality control is a crucial aspect of crowdsourcing systems; typically occupying a large fraction of the time and money invested on crowdsourcing. In this work, we devise techniques to generate confidence intervals for worker error rate estimates, thereby enabling a better evaluation of worker quality. We show that our techniques generate correct confidence intervals on a range of real-world datasets, and demonstrate wide applicability by using them to evict poorly performing workers, and provide confidence intervals on the accuracy of the answers.

Connected through hyperedge HE-1 (induced by source paper "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"):
  with members:
    - "crowd guilds worker led reputation and feedback on crowdsourcing platforms"
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "optimality of belief propagation for crowdsourced classification"
    - "embracing error to enable rapid crowdsourcing"
    - "crowd access path optimization diversity matters"
    - "incentivizing high quality crowdwork"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "how many workers to ask adaptive exploration for collecting high quality labels"

Connected through hyperedge HE-2 (induced by source paper "millionaire a hint guided approach for crowdsourcing"):
  with members:
    - "end to end learning for self driving cars"
    - "crowdsourcing with unsure option"
    - "on the convergence of a family of robust losses for stochastic gradient descent"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "incentivizing high quality crowdwork"
    - "training convolutional networks with noisy labels"
    - "imagenet large scale visual recognition challenge"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"

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"
    - "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"
    - "human powered sorts and joins"

Connected through hyperedge HE-4 (induced by source paper "fault tolerant entity resolution with the crowd"):
  with members:
    - "visual landmark recognition from internet photo collections a large scale evaluation"
    - "leveraging transitive relations for crowdsourced joins"
    - "crowder crowdsourcing entity resolution"
    - "large scale collective entity matching"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "globally optimal crowdsourcing quality management"):
  with members:
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-6 (induced by source paper "finish them pricing algorithms for human computation"):
  with members:
    - "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"
    - "human powered sorts and joins"

Connected through hyperedge HE-7 (induced by source paper "comprehensive and reliable crowd assessment algorithms"):
  with members:
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "human assisted graph search it s okay to ask questions"
    - "human powered sorts and joins"

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)