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 "crowd based multi predicate screening of papers in literature reviews"):
  Abstract: Systematic literature reviews (SLRs) are one of the most common and useful form of scientific research and publication. Tens of thousands of SLRs are published each year, and this rate is growing across all fields of science. Performing an accurate, complete and unbiased SLR is however a difficult and expensive endeavor. This is true in general for all phases of a literature review, and in particular for the paper screening phase, where authors lter a set of potentially in-scope papers based on a number of exclusion criteria. To address the problem, in recent years the research community has began to explore the use of the crowd to allow for a faster, accurate, cheaper and unbiased screening of papers. Initial results show that crowdsourcing can be effective, even for relatively complex reviews. In this paper we derive and analyze a set of strategies for crowd-based screening, and show that an adaptive strategy, that continuously re-assesses the statistical properties of the problem to minimize the number of votes needed to take decisions for each paper, significantly outperforms a number of non-adaptive approaches in terms of cost and accuracy. We validate both applicability and results of the approach through a set of crowdsourcing experiments, and discuss properties of the problem and algorithms that we believe to be generally of interest for classification problems where items are classified via a series of successive tests (as it often happens in medicine).

Includes members:
  Member "crowdsourcing paper screening in systematic literature reviews":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combining crowd and machines for multi predicate item screening")
  Member "crowdsourcing information extraction for biomedical systematic reviews":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combining crowd and machines for multi predicate item screening")
  Member "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combining crowd and machines for multi predicate item screening")

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.HC(Human-Computer Interaction)