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: "detecting and correcting for label shift with black box predictors"
  Abstract: Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels. Motivated by medical diagnosis, where diseases (targets), cause symptoms (observations), we focus on label shift, where the label marginal $p(y)$ changes but the conditional $p(x|y)$ does not. We propose Black Box Shift Estimation (BBSE) to estimate the test distribution $p(y)$. BBSE exploits arbitrary black box predictors to reduce dimensionality prior to shift correction. While better predictors give tighter estimates, BBSE works even when predictors are biased, inaccurate, or uncalibrated, so long as their confusion matrices are invertible. We prove BBSE's consistency, bound its error, and introduce a statistical test that uses BBSE to detect shift. We also leverage BBSE to correct classifiers. Experiments demonstrate accurate estimates and improved prediction, even on high-dimensional datasets of natural images

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"
    - "regularized learning for domain adaptation under label shifts"
    - "on learning invariant representation for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "addressing target shift in zero shot learning using grouped adversarial learning"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "semantic autoencoder for zero shot learning"
    - "adversarial discriminative domain adaptation"
    - "controllable invariance through adversarial feature learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "label embedding for image classification"

Connected through hyperedge HE-3 (induced by source paper "domain adaptation as a problem of inference on graphical models"):
  with members:
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "deep transfer learning with joint adaptation networks"
    - "coupled generative adversarial networks"
    - "deep coral correlation alignment for deep domain adaptation"
    - "domain separation networks"
    - "learning transferable features with deep adaptation networks"
    - "generative moment matching networks"
    - "a probabilistic calculus of actions"

Connected through hyperedge HE-4 (induced by source paper "a feedback shift correction in predicting conversion rates under delayed feedback"):
  with members:
    - "reacting to variations in product demand an application for conversion rate cr prediction in sponsored search"
    - "a nonparametric delayed feedback model for conversion rate prediction"
    - "field aware factorization machines in a real world online advertising system"
    - "stochastic bandit models for delayed conversions"
    - "online learning under delayed feedback"

Connected through hyperedge HE-5 (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"
    - "regularized learning for domain adaptation under label shifts"
    - "on calibration of modern neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "a label proportions estimation technique for adversarial domain adaptation in text classification"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial multi task learning for text classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a review of domain adaptation without target labels"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "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"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"

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"
    - "regularized learning for domain adaptation under label shifts"

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)