human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"):
  Abstract: In the setting where information cannot be verified, we propose a simple yet powerful information theoretical framework---the Mutual Information Paradigm---for information elicitation mechanisms. Our framework pays every agent a measure of mutual information between her signal and a peer's signal. We require that the mutual information measurement has the key property that any "data processing" on the two random variables will decrease the mutual information between them. We identify such information measures that generalize Shannon mutual information. #R##N#Our Mutual Information Paradigm overcomes the two main challenges in information elicitation without verification: (1) how to incentivize effort and avoid agents colluding to report random or identical responses (2) how to motivate agents who believe they are in the minority to report truthfully. #R##N#Aided by the information measures we found, (1) we use the paradigm to design a family of novel mechanisms where truth-telling is a dominant strategy and any other strategy will decrease every agent's expected payment (in the multi-question, detail free, minimal setting where the number of questions is large); (2) we show the versatility of our framework by providing a unified theoretical understanding of existing mechanisms---Peer Prediction [Miller 2005], Bayesian Truth Serum [Prelec 2004], and Dasgupta and Ghosh [2013]---by mapping them into our framework such that theoretical results of those existing mechanisms can be reconstructed easily. #R##N#We also give an impossibility result which illustrates, in a certain sense, the the optimality of our framework.

Includes members:
  Member "eliciting expertise without verification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dominantly truthful multi task peer prediction with a constant number of tasks")
  Member "water from two rocks maximizing the mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "peer prediction with heterogeneous tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "impact of social learning on privacy preserving data collection")
  Member "incentivizing evaluation via limited access to ground truth peer prediction makes things worse":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "credible sample elicitation")
  Member "putting peer prediction under the micro economic scope and making truth telling focal":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a technical survey on statistical modelling and design methods for crowdsourcing quality control")
  Member "informed truthfulness in multi task peer prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "surrogate scoring rules")
  Member "equilibrium selection in information elicitation without verification via information monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "surrogate scoring rules")
  Member "crowdsourced judgement elicitation with endogenous proficiency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "surrogate scoring rules")

Question: Please tell me which class the source paper 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.GT(Computer Science and Game Theory)