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: "does confidence reporting from the crowd benefit crowdsourcing performance"
  Abstract: We explore the design of an effective crowdsourcing system for an M-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final classification decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. Additionally, the workers report quantized confidence levels when they are able to submit definitive answers. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance. We obtain a couterintuitive result that the classification performance does not benefit from workers reporting quantized confidence. Therefore, the crowdsourcing system designer should employ the reject option without requiring confidence reporting.

Connected through hyperedge HE-1 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers"):
  with members:
    - "decision tree design for classification in crowdsourcing systems"
    - "optimal crowdsourced classification with a reject option in the presence of spammers"
    - "multi object classification via crowdsourcing with a reject option"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "reputation based incentive protocols in crowdsourcing applications"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-2 (induced by source paper "decision tree design for classification in crowdsourcing systems"):
  with members:
    - "optimal crowdsourced classification with a reject option in the presence of spammers"
    - "multi object classification via crowdsourcing with a reject option"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "cdas a crowdsourcing data analytics system"

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.SI(Social and Information Networks)