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: "estimating uncertainty online against an adversary"
  Abstract: Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems. Existing uncertainty estimation techniques may fail when their modeling assumptions are not met, e.g. when the data distribution differs from the one seen at training time. Here, we propose techniques that assess a classification algorithm's uncertainty via calibrated probabilities (i.e. probabilities that match empirical outcome frequencies in the long run) and which are guaranteed to be reliable (i.e. accurate and calibrated) on out-of-distribution input, including input generated by an adversary. This represents an extension of classical online learning that handles uncertainty in addition to guaranteeing accuracy under adversarial assumptions. We establish formal guarantees for our methods, and we validate them on two real-world problems: question answering and medical diagnosis from genomic data.

Connected through hyperedge HE-1 (induced by source paper "calibrated prediction with covariate shift via unsupervised domain adaptation"):
  with members:
    - "pac confidence sets for deep neural networks via calibrated prediction"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "conditional adversarial domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "conditional validity of inductive conformal predictors"

Connected through hyperedge HE-2 (induced by source paper "non parametric calibration for classification"):
  with members:
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "rotation equivariant cnns for digital pathology"
    - "on calibration of modern neural networks"
    - "on fairness and calibration"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "advances in variational inference"

Connected through hyperedge HE-3 (induced by source paper "calibrated model based deep reinforcement learning"):
  with members:
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "synthesizing neural network controllers with probabilistic model based reinforcement learning"
    - "model based reinforcement learning via meta policy optimization"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-4 (induced by source paper "accurate uncertainties for deep learning using calibrated regression"):
  with members:
    - "on calibration of modern neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "posterior calibration and exploratory analysis for natural language processing models"

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)