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 "calibration with bias corrected temperature scaling improves domain adaptation under label shift in modern neural networks"):
  Abstract: Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions while the conditional probability p(x|y) stays fixed. This is relevant in settings such as medical diagnosis, where a classifier trained to predict disease based on observed symptoms may need to be adapted to a different distribution where the baseline frequency of the disease is higher. Given calibrated estimates of p(y|x), one can apply an EM algorithm to correct for the shift in class imbalance between the training and test distributions without ever needing to calculate p(x|y). Unfortunately, modern neural networks typically fail to produce well-calibrated probabilities, compromising the effectiveness of this approach. Although Temperature Scaling can greatly reduce miscalibration in these networks, it can leave behind a systematic bias in the probabilities that still poses a problem. To address this, we extend Temperature Scaling with class-specific bias parameters, which largely eliminates systematic bias in the calibrated probabilities and allows for effective domain adaptation under label shift. We term our calibration approach "Bias-Corrected Temperature Scaling". On experiments with CIFAR10, we find that EM with Bias-Corrected Temperature Scaling significantly outperforms both EM with Temperature Scaling and the recently-proposed Black-Box Shift Estimation.

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 "detecting and correcting for label shift with black box predictors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift")
  Member "what is the effect of importance weighting in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "on calibration of modern neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "reliable confidence estimation via online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "em with bias corrected calibration is hard to beat at label shift adaptation")
  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)