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: "clamshell speeding up crowds for low latency data labeling"
  Abstract: Data labeling is a necessary but often slow process that impedes the development of interactive systems for modern data analysis. Despite rising demand for manual data labeling, there is a surprising lack of work addressing its high and unpredictable latency. In this paper, we introduce CLAMShell, a system that speeds up crowds in order to achieve consistently low-latency data labeling. We offer a taxonomy of the sources of labeling latency and study several large crowd-sourced labeling deployments to understand their empirical latency profiles. Driven by these insights, we comprehensively tackle each source of latency, both by developing novel techniques such as straggler mitigation and pool maintenance and by optimizing existing methods such as crowd retainer pools and active learning. We evaluate CLAMShell in simulation and on live workers on Amazon's Mechanical Turk, demonstrating that our techniques can provide an order of magnitude speedup and variance reduction over existing crowdsourced labeling strategies.

Connected through hyperedge HE-1 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors"):
  with members:
    - "cross modal data programming enables rapid medical machine learning"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "deep co training for semi supervised image recognition"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "inferring generative model structure with static analysis"
    - "snorkel rapid training data creation with weak supervision"
    - "morphnet fast simple resource constrained structure learning of deep networks"

Connected through hyperedge HE-2 (induced by source paper "crowdsourced truth discovery in the presence of hierarchies for knowledge fusion"):
  with members:
    - "truth finding on the deep web is the problem solved"
    - "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 "crowd powered data mining"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "multi label annotation aggregation in crowdsourcing"
    - "clustering via crowdsourcing"
    - "estimating the impact of unknown unknowns on aggregate query results"
    - "comprehensive and reliable crowd assessment algorithms"
    - "finish them pricing algorithms for human computation"
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-4 (induced by source paper "quality assured synchronized task assignment in crowdsourcing"):
  with members:
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-5 (induced by source paper "octopus a framework for cost quality time optimization in crowdsourcing"):
  with members:
    - "finish them pricing algorithms for human computation"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "point based pomdp algorithms improved analysis and implementation"
    - "crowdsourcing control moving beyond multiple choice"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "understanding workers developing effective tasks and enhancing marketplace dynamics a study of a large crowdsourcing marketplace"):
  with members:
    - "finish them pricing algorithms for human computation"
    - "cdas a crowdsourcing data analytics system"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-7 (induced by source paper "frog a fast and reliable crowdsourcing framework technical report"):
  with members:
    - "octopus a framework for cost quality time optimization in crowdsourcing"
    - "finish them pricing algorithms for human computation"
    - "crowdsourcing a word emotion association lexicon"
    - "cdas a crowdsourcing data analytics system"
    - "crowder crowdsourcing entity resolution"
    - "whom to ask jury selection for decision making tasks on micro blog services"
    - "analytic methods for optimizing realtime crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-8 (induced by source paper "qfix diagnosing errors through query histories"):
  with members:
    - "high level why not explanations using ontologies"
    - "perfxplain debugging mapreduce job performance"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"
    - "putting lipstick on pig enabling database style workflow provenance"
    - "explanation based auditing"

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)