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: "multi object classification via crowdsourcing with a reject option"
  Abstract: Consider designing an effective crowdsourcing system for   $M$   -ary classification where crowd workers complete simple binary microtasks, which are aggregated to give the final result. We consider the novel scenario where workers have a reject option, so they may skip microtasks they are unable or unwilling to do. For example, in mismatched speech transcription, workers who do not know the language may be unable to respond to microtasks in phonological dimensions outside their categorical perception. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize the crowd's classification performance. We evaluate system performance in both exact and asymptotic forms. Furthermore, we consider the setting where there may be a set of greedy workers that complete microtasks even when they are unable to perform it reliably. We consider an oblivious and an expurgation strategy to deal with greedy workers, developing an algorithm to adaptively switch between the two based on the estimated fraction of greedy workers in the anonymous crowd. Simulation results show improved performance compared with conventional majority voting.

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"
    - "does confidence reporting from the crowd benefit crowdsourcing performance"
    - "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 "sparse reject option classifier using successive linear programming"):
  with members:
    - "the data replication method for the classification with reject option"

Connected through hyperedge HE-3 (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"
    - "does confidence reporting from the crowd benefit crowdsourcing performance"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-4 (induced by source paper "does confidence reporting from the crowd benefit crowdsourcing performance"):
  with members:
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "reputation based incentive protocols in crowdsourcing applications"

Connected through hyperedge HE-5 (induced by source paper "universal clustering via crowdsourcing"):
  with members:
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "budget optimal task allocation for reliable crowdsourcing systems"

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.LG(Machine Learning)