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: "the importance of being earnest in crowdsourcing systems"
  Abstract: This paper presents the first systematic investigation of the potential performance gains for crowdsourcing systems, deriving from available information at the requester about individual worker earnestness (reputation). In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple ``maximum a-posteriori`` decision rule. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the LRA decision rule introduced in the literature.

Connected through hyperedge HE-1 (induced by source paper "game theoretic design of optimal two sided rating protocols for service exchange dilemma in crowdsourcing"):
  with members:
    - "utility fair rate allocation in lte 802 11 networks"
    - "iterated crowdsourcing dilemma game"
    - "socially optimal design of service exchange platforms with imperfect monitoring"

Connected through hyperedge HE-2 (induced by source paper "the importance of worker reputation information in microtask based crowd work systems"):
  with members:
    - "bandit based task assignment for heterogeneous crowdsourcing"
    - "incentivizing high quality crowdwork"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-3 (induced by source paper "a formal framework for modeling trust and reputation in collective adaptive systems"):
  with members:
    - "mela modelling in ecology with location attributes"
    - "carma collective adaptive resource sharing markovian agents"
    - "strategic analysis of trust models for user centric networks"

Connected through hyperedge HE-4 (induced by source paper "incentive mechanisms for participatory sensing survey and research challenges"):
  with members:
    - "providing long term participation incentive in participatory sensing"
    - "sacrm social aware crowdsourcing with reputation management in mobile sensing"
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"
    - "motivating smartphone collaboration in data acquisition and distributed computing"
    - "mining users significant driving routes with low power sensors"
    - "fairness and social welfare in incentivizing participatory sensing"
    - "truthful scheduling mechanisms for powering mobile crowdsensing"
    - "mechanism design for time critical and cost critical task execution via crowdsourcing"

Connected through hyperedge HE-5 (induced by source paper "multi round master worker computing a repeated game approach"):
  with members:
    - "rational proofs with multiple provers"

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.HC(Human-Computer Interaction)