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: "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.

Connected through hyperedge HE-1 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "domain agnostic learning with disentangled representations"
    - "robust natural language inference models with example forgetting"
    - "batch weight for domain adaptation with mass shift"
    - "learning to transfer examples for partial domain adaptation"
    - "learning representations by maximizing mutual information across views"
    - "on learning invariant representation for domain adaptation"
    - "multi source domain adaptation with mixture of experts"

Connected through hyperedge HE-2 (induced by source paper "leep a new measure to evaluate transferability of learned representations"):
  with members:
    - "dataset2vec learning dataset meta features"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "a baseline for few shot image classification"
    - "which tasks should be learned together in multi task learning"
    - "p2l predicting transfer learning for images and semantic relations"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement"):
  with members:
    - "mba mini batch auc optimization"
    - "training set debugging using trusted items"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "xgboost a scalable tree boosting system"
    - "one pass auc optimization"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-4 (induced by source paper "a unified view of label shift estimation"):
  with members:
    - "calibration with bias corrected temperature scaling improves domain adaptation under label shift in modern neural networks"
    - "detecting and correcting for label shift with black box predictors"
    - "on calibration of modern neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "a label proportions estimation technique for adversarial domain adaptation in text classification"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "detecting and correcting for label shift with black box predictors"
    - "adversarial multi task learning for text classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a review of domain adaptation without target labels"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "theoretical perspective of deep domain adaptation"
    - "deep visual domain adaptation a survey"
    - "characterizing and avoiding negative transfer"
    - "detecting and correcting for label shift with black box predictors"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "meta learning with latent embedding optimization"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-8 (induced by source paper "learning imbalanced datasets with label distribution aware margin loss"):
  with members:
    - "diversity in faces"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "overfitting of neural nets under class imbalance analysis and improvements for segmentation"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "meta weight net learning an explicit mapping for sample weighting"

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)