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: "reply supply efficient crowdsourcing when workers do more than answer questions"
  Abstract: Crowdsourcing works by distributing many small tasks to large numbers of workers, yet the true potential of crowdsourcing lies in workers doing more than performing simple tasks-they can apply their experience and creativity to provide new and unexpected information to the crowdsourcer. One such case is when workers not only answer a crowdsourcer's questions but also contribute new questions for subsequent crowd analysis, leading to a growing set of questions. This growth creates an inherent bias for early questions since a question introduced earlier by a worker can be answered by more subsequent workers than a question introduced later. Here we study how to perform efficient crowdsourcing with such growing question sets. By modeling question sets as networks of interrelated questions, we introduce algorithms to help curtail the growth bias by efficiently distributing workers between exploring new questions and addressing current questions. Experiments and simulations demonstrate that these algorithms can efficiently explore an unbounded set of questions without losing confidence in crowd answers.

Connected through hyperedge HE-1 (induced by source paper "accurate inference of crowdsourcing properties when using efficient allocation strategies"):
  with members:
    - "efficient crowd exploration of large networks the case of causal attribution"
    - "crowdsourcing predictors of residential electric energy usage"
    - "crowdsourcing predictors of behavioral outcomes"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-2 (induced by source paper "efficient crowdsourcing of crowd generated microtasks"):
  with members:
    - "efficient crowd exploration of large networks the case of causal attribution"
    - "crowdsourcing predictors of residential electric energy usage"
    - "crowdsourcing predictors of behavioral outcomes"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-3 (induced by source paper "efficient crowd exploration of large networks the case of causal attribution"):
  with members:
    - "crowd ideation of supervised learning problems"
    - "crowdsourcing predictors of residential electric energy usage"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "crowdsourcing predictors of behavioral outcomes"
    - "search in power law networks"

Connected through hyperedge HE-4 (induced by source paper "autocompletion interfaces make crowd workers slower but their use promotes response diversity"):
  with members:
    - "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.SI(Social and Information Networks)