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: "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.

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"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "incentives for truthful peer grading"
    - "informed truthfulness in multi task peer prediction"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-3 (induced by source paper "peer loss functions learning from noisy labels without knowing noise rates"):
  with members:
    - "l_dmi an information theoretic noise robust loss function"
    - "water from two rocks maximizing the mutual information"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "informed truthfulness in multi task peer prediction"
    - "learning with symmetric label noise the importance of being unhinged"
    - "deep residual learning for image recognition"
    - "making risk minimization tolerant to label noise"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-4 (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"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "incentives for truthful peer grading"

Connected through hyperedge HE-5 (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"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-6 (induced by source paper "max mig an information theoretic approach for joint learning from crowds"):
  with members:
    - "water from two rocks maximizing the mutual information"
    - "learning from noisy singly labeled data"
    - "who said what modeling individual labelers improves classification"
    - "achieving budget optimality with adaptive schemes in crowdsourcing"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-7 (induced by source paper "obtaining costly unverifiable valuations from a single agent"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "optimal mechanisms for selling information"

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)