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: "water from two rocks maximizing the mutual information"
  Abstract: We build a natural connection between the learning problem, co-training, and forecast elicitation without verification (related to peer-prediction) and address them simultaneously using the same information theoretic approach. #R##N#In co-training/multiview learning, the goal is to aggregate two views of data into a prediction for a latent label. We show how to optimally combine two views of data by reducing the problem to an optimization problem. Our work gives a unified and rigorous approach to the general setting. #R##N#In forecast elicitation without verification we seek to design a mechanism that elicits high quality forecasts from agents in the setting where the mechanism does not have access to the ground truth. By assuming the agents' information is independent conditioning on the outcome, we propose mechanisms where truth-telling is a strict equilibrium for both the single-task and multi-task settings. Our multi-task mechanism additionally has the property that the truth-telling equilibrium pays better than any other strategy profile and strictly better than any other "non-permutation" strategy profile when the prior satisfies some mild conditions.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "credible sample elicitation"):
  with members:
    - "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"
    - "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"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"

Connected through hyperedge HE-3 (induced by source paper "surrogate scoring rules"):
  with members:
    - "deep bayesian trust a dominant and fair incentive mechanism for crowd"
    - "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"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-4 (induced by source paper "l_dmi an information theoretic noise robust loss function"):
  with members:
    - "max mig an information theoretic approach for joint learning from crowds"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "masking a new perspective of noisy supervision"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-5 (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"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "informed truthfulness in multi task peer prediction"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "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-6 (induced by source paper "credible sample elicitation by deep learning for deep learning"):
  with members:
    - "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"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"

Connected through hyperedge HE-7 (induced by source paper "securely trading unverifiable information without trust"):
  with members:
    - "a scalable verification solution for blockchains"
    - "astraea a decentralized blockchain oracle"
    - "betrayal distrust and rationality smart counter collusion contracts for verifiable cloud computing"
    - "smart contract templates essential requirements and design options"
    - "informed truthfulness in multi task peer prediction"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "a new approach to practical active secure two party computation"

Connected through hyperedge HE-8 (induced by source paper "dominantly truthful multi task peer prediction with a constant number of tasks"):
  with members:
    - "eliciting expertise without verification"
    - "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"
    - "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"

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.LG(Machine Learning)