human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "em with bias corrected calibration is hard to beat at label shift adaptation"):
  Abstract: Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to scenarios where the baseline prevalence of the disease is different. Given estimates of p(y|x) from a predictive model, Saerens et al. (2002) proposed an efficient EM algorithm to correct for label shift that does not require model retraining. A limiting assumption of this algorithm is that p(y|x) is calibrated, which is not true of modern neural networks. Recently, Black Box Shift Learning (BBSL) (Lipton et al., 2018) and Regularized Learning under Label Shifts (RLLS) (Azizzadenesheli et al., 2019) have emerged as state-of-the-art techniques to cope with label shift when a classifier does not output calibrated probabilities. However, both BBSL and RLLS require model retraining with importance weights, which poses challenges in practice (Byrd and Lipton, 2019), and neither has been benchmarked against EM. Here we show that by combining EM with a type of calibration we call bias-corrected calibration, we outperform both BBSL and RLLS across diverse datasets and distribution shifts. We further show that the EM objective is concave and bounded, and introduce a theoretically principled strategy for estimating source-domain priors that improves robustness to poor calibration. This work demonstrates that EM with appropriate calibration is a formidable and efficient baseline that future work in label shift adaptation should be compared against. #R##N#Colab notebooks reproducing experiments are available at (anonymized link): this https URL

Includes members:
  Member "evaluating model calibration in classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mix n match ensemble and compositional methods for uncertainty calibration in deep learning")
  Member "regularized learning for domain adaptation under label shifts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift")
  Member "on calibration of modern neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "selective classification for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "reliable confidence estimation via online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "calibration with bias corrected temperature scaling improves domain adaptation under label shift in modern neural networks")
  Member "on causal and anticausal learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised disentanglement without compromises")

Question: Please tell me which class the source paper 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)