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: "crowdsourcing control moving beyond multiple choice"
  Abstract: To ensure quality results from crowdsourced tasks, requesters often aggregate worker responses and use one of a plethora of strategies to infer the correct answer from the set of noisy responses. However, all current models assume prior knowledge of all possible outcomes of the task. While not an unreasonable assumption for tasks that can be posited as multiple-choice questions (e.g. n-ary classification), we observe that many tasks do not naturally fit this paradigm, but instead demand a free-response formulation where the outcome space is of infinite size (e.g. audio transcription). We model such tasks with a novel probabilistic graphical model, and design and implement LazySusan, a decision-theoretic controller that dynamically requests responses as necessary in order to infer answers to these tasks. We also design an EM algorithm to jointly learn the parameters of our model while inferring the correct answers to multiple tasks at a time. Live experiments on Amazon Mechanical Turk demonstrate the superiority of LazySusan at solving SAT Math questions, eliminating 83.2% of the error and achieving greater net utility compared to the state-ofthe-art strategy, majority-voting. We also show in live experiments that our EM algorithm outperforms majority-voting on a visualization task that we design.

Connected through hyperedge HE-1 (induced by source paper "octopus a framework for cost quality time optimization in crowdsourcing"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "finish them pricing algorithms for human computation"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "point based pomdp algorithms improved analysis and implementation"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "optimizing open ended crowdsourcing the next frontier in crowdsourced data management"):
  with members:
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "a large annotated corpus for learning natural language inference"
    - "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"
    - "human assisted graph search it s okay to ask questions"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-3 (induced by source paper "finish them pricing algorithms for human computation"):
  with members:
    - "evaluating the crowd with confidence"
    - "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"

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.AI(Artificial Intelligence)