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: "differentially private fair learning"
  Abstract: Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy. We design two learning algorithms that simultaneously promise differential privacy and equalized odds, a 'fairness' condition that corresponds to equalizing false positive and negative rates across protected groups. Our first algorithm is a private implementation of the equalized odds post-processing approach of [Hardt et al., 2016]. This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of 'disparate treatment'. Our second algorithm is a differentially private version of the oracle-efficient in-processing approach of [Agarwal et al., 2018] that can be used to find the optimal fair classifier, given access to a subroutine that can solve the original (not necessarily fair) learning problem. This algorithm is more complex but need not have access to protected group membership at test time. We identify new tradeoffs between fairness, accuracy, and privacy that emerge only when requiring all three properties, and show that these tradeoffs can be milder if group membership may be used at test time. We conclude with a brief experimental evaluation.

Connected through hyperedge HE-1 (induced by source paper "removing disparate impact of differentially private stochastic gradient descent on model accuracy"):
  with members:
    - "differentially private and fair classification via calibrated functional mechanism"
    - "differential privacy has disparate impact on model accuracy"
    - "differentially private learning with adaptive clipping"
    - "robust anomaly detection and backdoor attack detection via differential privacy"
    - "adaclip adaptive clipping for private sgd"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-2 (induced by source paper "fair learning with private demographic data"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "the cost of a reductions approach to private fair optimization"
    - "noise tolerant fair classification"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "a reductions approach to fair classification"
    - "proxy fairness"
    - "equality of opportunity in supervised learning"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-3 (induced by source paper "boosted and differentially private ensembles of decision trees"):
  with members:
    - "differentially private learning of geometric concepts"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "training dnns with hybrid block floating point"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "differentially private random decision forests using smooth sensitivity"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "differential privacy has disparate impact on model accuracy"):
  with members:
    - "diversity in faces"
    - "fair decision making using privacy protected data"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "class balanced loss based on effective number of samples"
    - "agnostic federated learning"
    - "federated heavy hitters discovery with differential privacy"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "adversarial removal of demographic attributes from text data"

Connected through hyperedge HE-5 (induced by source paper "auditing and achieving intersectional fairness in classification problems"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "fairvis visual analytics for discovering intersectional bias in machine learning"
    - "multiaccuracy black box post processing for fairness in classification"
    - "automated data slicing for model validation a big data ai integration approach"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "an intersectional definition of fairness"

Connected through hyperedge HE-6 (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"
    - "fairness through computationally bounded awareness"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-7 (induced by source paper "multi differential fairness auditor for black box classifiers"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "the frontiers of fairness in machine learning"
    - "fairness through computationally bounded awareness"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "fault tolerance of neural networks in adversarial settings"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "evaluating differentially private machine learning in practice"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial fault tolerant training for deep neural networks"
    - "privacy risks of explaining machine learning models"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "renyi differential privacy"

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)