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 "regularized learning for domain adaptation under label shifts"):
  Abstract: We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain. We first estimate importance weights using labeled source data and unlabeled target data, and then train a classifier on the weighted source samples. We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class. To the best of our knowledge, this is the first generalization bound for the label-shift problem where the labels in the target domain are not available. Based on this bound, we propose a regularized estimator for the small-sample regime which accounts for the uncertainty in the estimated weights. Experiments on the CIFAR-10 and MNIST datasets show that RLLS improves classification accuracy, especially in the low sample and large-shift regimes, compared to previous methods.

Includes members:
  Member "detecting and correcting for label shift with black box predictors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift")
  Member "active learning for cost sensitive classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep active learning for remote sensing object detection")
  Member "reinforcement learning of pomdp s using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "reinforcement learning of pomdps using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "mixture proportion estimation via kernel embedding of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards mixture proportion estimation without irreducibility")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "statistical linear estimation with penalized estimators an application to reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "information theoretic considerations in batch reinforcement learning")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provable meta learning of linear representations")

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)