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: "fairness through computationally bounded awareness"
  Abstract: We study the problem of fair classification within the versatile framework of Dwork et al. [ITCS 2012], which assumes the existence of a metric that measures similarity between pairs of individuals. Unlike previous works on metric-based fairness, we do not assume that the entire metric is known to the learning algorithm. Instead, we study the setting where a learning algorithm can query this metric a bounded number of times to ascertain similarities between particular pairs of individuals. For example, the queries might be answered by a panel of specialists spanning social scientists, statisticians, demographers, and ethicists. #R##N#We propose "metric multifairness," a new definition of fairness that is parameterized by a similarity metric $\delta$ on pairs of individuals and a collection ${\cal C}$ of"comparison sets" over pairs of individuals. One way to view this collection is as the family of comparisons that can be expressed within some computational bound. With this interpretation, metric multifairnesss loosely guarantees that similar subpopulations are treated similarly, as long as these subpopulations can be identified within this bound. In particular, metric multifairness implies that a rich class of subpopulations are protected from a multitude of discriminatory behaviors. #R##N#We provide a general-purpose framework for learning a metric multifair hypothesis that achieves near-optimal loss from a small number of random samples from the metric $\delta$. We study the sample complexity and time complexity of learning a metric multifair hypothesis (providing rather tight upper and lower bounds) by connecting it to the task of learning the class ${\cal C}$. In particular, if the class ${\cal C}$ admits an efficient agnostic learner, then we can learn such a metric multifair hypothesis efficiently.

Connected through hyperedge HE-1 (induced by source paper "personalization of deep learning"):
  with members:
    - "human to ai coach improving human inputs to ai systems"
    - "personalized explanation in machine learning"
    - "transfer adaptation learning a decade survey"
    - "explainable recommendation a survey and new perspectives"
    - "small sample learning in big data era"
    - "automated curriculum learning for neural networks"
    - "unsupervised domain adaptation for robust speech recognition via variational autoencoder based data augmentation"
    - "learning by asking questions"

Connected through hyperedge HE-2 (induced by source paper "testing dnn image classifier for confusion bias errors"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "metric learning for adversarial robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "noise tolerant fair classification"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "visual interpretability for deep learning a survey"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "noise tolerant fair classification"):
  with members:
    - "agnostic federated learning"
    - "fairness risk measures"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "differentially private fair learning"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-4 (induced by source paper "tracking and improving information in the service of fairness"):
  with members:
    - "algorithmic fairness"
    - "50 years of test un fairness lessons for machine learning"
    - "delayed impact of fair machine learning"
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "downstream effects of affirmative action"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the price of fair pca one extra dimension"
    - "access to population level signaling as a source of inequality"

Connected through hyperedge HE-5 (induced by source paper "the random conditional distribution for higher order probabilistic inference"):
  with members:
    - "soft constraints for inference with declarative knowledge"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "algorithmic decision making and the cost of fairness"
    - "practical optimal experiment design with probabilistic programs"
    - "equality of opportunity in supervised learning"
    - "a confidence based approach for balancing fairness and accuracy"
    - "venture a higher order probabilistic programming platform with programmable inference"

Connected through hyperedge HE-6 (induced by source paper "leveraging semi supervised learning for fairness using neural networks"):
  with members:
    - "improved adversarial learning for fair classification"
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "a neural network framework for fair classifier"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"

Connected through hyperedge HE-7 (induced by source paper "advancing subgroup fairness via sleeping experts"):
  with members:
    - "individual fairness in hindsight"
    - "multiaccuracy black box post processing for fairness in classification"
    - "online learning with an unknown fairness metric"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "an algorithmic framework to control bias in bandit based personalization"
    - "on preserving non discrimination when combining expert advice"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-8 (induced by source paper "multi differential fairness auditor for black box classifiers"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "differentially private fair learning"
    - "the frontiers of fairness in machine learning"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "an intersectional definition of fairness"
    - "a reductions approach to fair classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "calibration for the computationally identifiable masses"

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)