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: "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
  Abstract: We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study the problem of training non-convex models subject to these rate constraints (or any non-convex and non-differentiable constraints). In the non-convex setting, the standard approach of Lagrange multipliers may fail. Furthermore, if the constraints are non-differentiable, then one cannot optimize the Lagrangian with gradient-based methods. To solve these issues, we introduce the proxy-Lagrangian formulation. This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem. We then give a procedure which shrinks the randomized solution down to one that is a mixture of at most $m+1$ deterministic solutions, given $m$ constraints. This culminates in algorithms that can solve non-convex constrained optimization problems with possibly non-differentiable and non-convex constraints with theoretical guarantees. We provide extensive experimental results enforcing a wide range of policy goals including different fairness metrics, and other goals on accuracy, coverage, recall, and churn.

Connected through hyperedge HE-1 (induced by source paper "deontological ethics by monotonicity shape constraints"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fairness of exposure in rankings"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "a convex framework for fair regression"
    - "ranking with fairness constraints"
    - "fairness in machine learning lessons from political philosophy"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-2 (induced by source paper "identifying and correcting label bias in machine learning"):
  with members:
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "detecting zones and threat on 3d body in security airports using deep learning machine"
    - "detecting zones and threat on 3d body for security in airports using deep machine learning"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-3 (induced by source paper "pairwise fairness for ranking and regression"):
  with members:
    - "fair regression quantitative definitions and reduction based algorithms"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "policy learning for fairness in ranking"
    - "equity of attention amortizing individual fairness in rankings"
    - "reducing disparate exposure in ranking a learning to rank approach"
    - "fairness of exposure in rankings"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-4 (induced by source paper "optimizing nondecomposable data dependent regularizers via lagrangian reparameterization offers significant performance and efficiency gains"):
  with members:
    - "differentiable sorting using optimal transport the sinkhorn cdf and quantile operator"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "object detection with deep learning a review"
    - "convex constrained sparse additive modeling and its extensions"
    - "ldmnet low dimensional manifold regularized neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "automatic differentiation in machine learning a survey"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "group based fair learning leads to counter intuitive predictions"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "delayed impact of fair machine learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "optimizing generalized rate metrics through game equilibrium"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "equality of opportunity in supervised learning"
    - "optimizing text quantifiers for multivariate loss functions"
    - "optimizing f measure a tale of two approaches"

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)