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: "crowdsourcing predictors of behavioral outcomes"
  Abstract: Generating models from large data sets-and determining which subsets of data to mine-is becoming increasingly automated. However, choosing what data to collect in the first place requires human intuition or experience, usually supplied by a domain expert. This paper describes a new approach to machine science which demonstrates for the first time that nondomain experts can collectively formulate features and provide values for those features such that they are predictive of some behavioral outcome of interest. This was accomplished by building a Web platform in which human groups interact to both respond to questions likely to help predict a behavioral outcome and pose new questions to their peers. This results in a dynamically growing online survey, but the result of this cooperative behavior also leads to models that can predict the user's outcomes based on their responses to the user-generated survey questions. Here, we describe two Web-based experiments that instantiate this approach: The first site led to models that can predict users' monthly electric energy consumption, and the other led to models that can predict users' body mass index. As exponential increases in content are often observed in successful online collaborative communities, the proposed methodology may, in the future, lead to similar exponential rises in discovery and insight into the causal factors of behavioral outcomes.

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"
    - "reply supply efficient crowdsourcing when workers do more than answer questions"
    - "crowdsourcing predictors of residential electric energy usage"
    - "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"
    - "reply supply efficient crowdsourcing when workers do more than answer questions"
    - "crowdsourcing predictors of residential electric energy usage"
    - "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"
    - "reply supply efficient crowdsourcing when workers do more than answer questions"
    - "crowdsourcing predictors of residential electric energy usage"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "search in power law networks"

Connected through hyperedge HE-4 (induced by source paper "reply supply efficient crowdsourcing when workers do more than answer questions"):
  with members:
    - "quizz targeted crowdsourcing with a billion potential users"
    - "adaptive crowdsourcing algorithms for the bandit survey problem"
    - "distributed representations of words and phrases and their compositionality"
    - "time critical social mobilization"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "crowdsourcing predictors of residential electric energy usage"):
  with members:
    - "time critical social mobilization"

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.CY(Computers and Society)