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: "reliable confidence estimation via online learning"
  Abstract: Assessing uncertainty within machine learning systems is an important step towards ensuring their safety and reliability. 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. Our methods admit formal bounds that can serve as confidence intervals, and process data in an online manner, which obviates the need for a separate calibration dataset. We establish theoretical guarantees on our methods' accuracies and convergence rates, 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 "calibration with bias corrected temperature scaling improves domain adaptation under label shift in modern neural networks"):
  with members:
    - "evaluating model calibration in classification"
    - "detecting and correcting for label shift with black box predictors"
    - "what is the effect of importance weighting in deep learning"
    - "on calibration of modern neural networks"
    - "on causal and anticausal learning"

Connected through hyperedge HE-2 (induced by source paper "em with bias corrected calibration is hard to beat at label shift adaptation"):
  with members:
    - "evaluating model calibration in classification"
    - "regularized learning for domain adaptation under label shifts"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"
    - "on causal and anticausal learning"

Connected through hyperedge HE-3 (induced by source paper "on calibration of modern neural networks"):
  with members:
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "regularizing neural networks by penalizing confident output distributions"
    - "deep networks with stochastic depth"
    - "end to end learning for self driving cars"
    - "understanding deep learning requires rethinking generalization"
    - "learning scalable deep kernels with recurrent structure"
    - "densely connected convolutional networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

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)