human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "regularized minimax conditional entropy for crowdsourcing"):
  Abstract: There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we derive a unique probabilistic labeling model jointly parameterized by worker ability and item difficulty. We also propose an objective measurement principle, and show that our method is the only method which satisfies this objective measurement principle. We validate our method through a variety of real crowdsourcing datasets with binary, multiclass or ordinal labels.

Includes members:
  Member "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the complexity of contracts")
  Member "double or nothing multiplicative incentive mechanisms for crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers")
  Member "adaptive crowdsourcing algorithms for the bandit survey problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reply supply efficient crowdsourcing when workers do more than answer questions")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning latent parameters without human response patterns item response theory with artificial crowds")
  Member "budget optimal task allocation for reliable crowdsourcing systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crowdsourced classification with xor queries fundamental limits and an efficient algorithm")

Question: Please tell me which class the source paper 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.LG(Machine Learning)