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: "identifying and correcting label bias in machine learning"
  Abstract: Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematical formulation of how this bias can arise. We do so by assuming the existence of underlying, unknown, and unbiased labels which are overwritten by an agent who intends to provide accurate labels but may have biases against certain groups. Despite the fact that we only observe the biased labels, we are able to show that the bias may nevertheless be corrected by re-weighting the data points without changing the labels. We show, with theoretical guarantees, that training on the re-weighted dataset corresponds to training on the unobserved but unbiased labels, thus leading to an unbiased machine learning classifier. Our procedure is fast and robust and can be used with virtually any learning algorithm. We evaluate on a number of standard machine learning fairness datasets and a variety of fairness notions, finding that our method outperforms standard approaches in achieving fair classification.

Connected through hyperedge HE-1 (induced by source paper "fr train a mutual information based approach to fair and robust training"):
  with members:
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "learning to reweight examples for robust deep learning"
    - "the frontiers of fairness in machine learning"
    - "two player games for efficient non convex constrained optimization"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "on fairness and calibration"

Connected through hyperedge HE-2 (induced by source paper "getting fairness right towards a toolbox for practitioners"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "equality of opportunity in supervised learning"
    - "semantics derived automatically from language corpora necessarily contain human biases"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "discrimination in online ad delivery"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "controlling biases and diversity in diverse image to image translation"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "women also snowboard overcoming bias in captioning models"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "weakly supervised domain specific color naming based on attention"

Connected through hyperedge HE-4 (induced by source paper "does object recognition work for everyone"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "on fairness and calibration"
    - "recognizing and curating photo albums via event specific image importance"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "algorithmic decision making and the cost of fairness"
    - "word translation without parallel data"

Connected through hyperedge HE-5 (induced by source paper "towards debiasing fact verification models"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "probing neural network comprehension of natural language arguments"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "counterfactual reasoning for fair clinical risk prediction"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "a framework for understanding unintended consequences of machine learning"
    - "adversarial logit pairing"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "causal reasoning for algorithmic fairness"
    - "counterfactual fairness in text classification through robustness"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "the frontiers of fairness in machine learning"

Connected through hyperedge HE-7 (induced by source paper "recovering from biased data can fairness constraints improve accuracy"):
  with members:
    - "discriminative but not discriminatory a comparison of fairness definitions under different worldviews"
    - "learning under selective labels in the presence of expert consistency"
    - "selection problems in the presence of implicit bias"
    - "on fairness and calibration"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-8 (induced by source paper "group based fair learning leads to counter intuitive predictions"):
  with members:
    - "delayed impact of fair machine learning"
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

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)