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: "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
  Abstract: In many settings, an effective way of evaluating objects of interest is to collect evaluations from dispersed individuals and to aggregate these evaluations together. Some examples are categorizing online content and evaluating student assignments via peer grading. For this data science problem, one challenge is to motivate participants to conduct such evaluations carefully and to report them honestly, particularly when doing so is costly. Existing approaches, notably peer-prediction mechanisms, can incentivize truth telling in equilibrium. However, they also give rise to equilibria in which agents do not pay the costs required to evaluate accurately, and hence fail to elicit useful information. We show that this problem is unavoidable whenever agents are able to coordinate using low-cost signals about the items being evaluated (e.g., text labels or pictures). We then consider ways of circumventing this problem by comparing agents' reports to ground truth, which is available in practice when there exist trusted evaluators---such as teaching assistants in the peer grading scenario---who can perform a limited number of unbiased (but noisy) evaluations. Of course, when such ground truth is available, a simpler approach is also possible: rewarding each agent based on agreement with ground truth with some probability, and unconditionally rewarding the agent otherwise. Surprisingly, we show that the simpler mechanism achieves stronger incentive guarantees given less access to ground truth than a large set of peer-prediction mechanisms.

Connected through hyperedge HE-1 (induced by source paper "credible sample elicitation"):
  with members:
    - "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "density functional estimators with k nearest neighbor bandwidths"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-2 (induced by source paper "surrogate scoring rules"):
  with members:
    - "deep bayesian trust a dominant and fair incentive mechanism for crowd"
    - "water from two rocks maximizing the mutual information"
    - "incentives for truthful peer grading"
    - "informed truthfulness in multi task peer prediction"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-3 (induced by source paper "credible sample elicitation by deep learning for deep learning"):
  with members:
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "approximation and convergence properties of generative adversarial learning"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "incentives for truthful peer grading"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"

Connected through hyperedge HE-4 (induced by source paper "dominantly truthful multi task peer prediction with a constant number of tasks"):
  with members:
    - "eliciting expertise without verification"
    - "water from two rocks maximizing the mutual information"
    - "informed truthfulness in multi task peer prediction"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-5 (induced by source paper "report sensitive spot checking in peer grading systems"):
  with members:
    - "incentives for truthful peer grading"
    - "informed truthfulness in multi task peer prediction"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-6 (induced by source paper "deep bayesian trust a dominant and fair incentive mechanism for crowd"):
  with members:
    - "peer prediction with heterogeneous tasks"
    - "incentives for truthful evaluations"
    - "informed truthfulness in multi task peer prediction"
    - "a survey on truth discovery"
    - "incentivizing high quality crowdwork"
    - "fairness through awareness"

Connected through hyperedge HE-7 (induced by source paper "incentivizing effort and precision in peer grading"):
  with members:
    - "informed truthfulness in multi task peer prediction"
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "methods for ordinal peer grading"
    - "tuned models of peer assessment in moocs"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-8 (induced by source paper "water from two rocks maximizing the mutual information"):
  with members:
    - "eliciting expertise without verification"
    - "informed truthfulness in multi task peer prediction"
    - "a survey on multi view learning"
    - "crowdsourced judgement elicitation with endogenous proficiency"

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.GT(Computer Science and Game Theory)