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 "combining crowd and machines for multi predicate item screening"):
  Abstract: This paper discusses how crowd and machine classifiers can be efficiently combined to screen items that satisfy a set of predicates. We show that this is a recurring problem in many domains, present machine-human (hybrid) algorithms that screen items efficiently and estimate the gain over human-only or machine-only screening in terms of performance and cost. We further show how, given a new classification problem and a set of classifiers of unknown accuracy for the problem at hand, we can identify how to manage the cost-accuracy trade off by progressively determining if we should spend budget to obtain test data (to assess the accuracy of the given classifiers), or to train an ensemble of classifiers, or whether we should leverage the existing machine classifiers with the crowd, and in this case how to efficiently combine them based on their estimated characteristics to obtain the classification. We demonstrate that the techniques we propose obtain significant cost/accuracy improvements with respect to the leading classification algorithms.

Includes members:
  Member "crowd based multi predicate screening of papers in literature reviews":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "crowdrev a platform for crowd based screening of literature reviews")
  Member "crowdsourcing paper screening in systematic literature reviews":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding the impact of text highlighting in crowdsourcing tasks")
  Member "optimality of belief propagation for crowdsourced classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crowdsourced classification with xor queries fundamental limits and an efficient algorithm")
  Member "crowdsourcing information extraction for biomedical systematic reviews":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crowdrev a platform for crowd based screening of literature reviews")
  Member "data fusion resolving conflicts from multiple sources":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "functional dependencies unleashed for scalable data exchange")
  Member "crowd access path optimization diversity matters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions")
  Member "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "practice of efficient data collection via crowdsourcing at large scale")
  Member "crowder crowdsourcing entity resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crowdsourced collective entity resolution with relational match propagation")

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.IR(Information Retrieval)