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: "two player games for efficient non convex constrained optimization"
  Abstract: In recent years, constrained optimization has become increasingly relevant to the machine learning community, with applications including Neyman-Pearson classification, robust optimization, and fair machine learning. A natural approach to constrained optimization is to optimize the Lagrangian, but this is not guaranteed to work in the non-convex setting. Instead, we prove that, given a Bayesian optimization oracle, a modified Lagrangian approach can be used to find a distribution over no more than m+1 models (where m is the number of constraints) that is nearly-optimal and nearly-feasible w.r.t. the original constrained problem. Interestingly, our method can be extended to non-differentiable--even discontinuous--constraints (where assuming a Bayesian optimization oracle is not realistic) by viewing constrained optimization as a non-zero-sum two-player game. The first player minimizes external regret in terms of easy-to-optimize "proxy constraints", while the second player enforces the original constraints by minimizing swap-regret.

Connected through hyperedge HE-1 (induced by source paper "fr train a mutual information based approach to fair and robust training"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "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"
    - "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 "deontological ethics by monotonicity shape constraints"):
  with members:
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fairness of exposure in rankings"
    - "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-3 (induced by source paper "robust optimization for fairness with noisy protected groups"):
  with members:
    - "pairwise fairness for ranking and regression"
    - "proxy fairness"
    - "equality of opportunity in supervised learning"
    - "satisfying real world goals with dataset constraints"
    - "fairness through awareness"

Connected through hyperedge HE-4 (induced by source paper "fair bandit learning with delayed impact of actions"):
  with members:
    - "regret bounds for restless markov bandits"

Connected through hyperedge HE-5 (induced by source paper "identifying and correcting label bias in machine learning"):
  with members:
    - "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"
    - "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"
    - "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-6 (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"
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "equity of attention amortizing individual fairness in rankings"
    - "reducing disparate exposure in ranking a learning to rank approach"
    - "fairness of exposure in rankings"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-7 (induced by source paper "equal opportunity in online classification with partial feedback"):
  with members:
    - "a reductions approach to fair classification"
    - "calibrated fairness in bandits"
    - "runaway feedback loops in predictive policing"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "from average case complexity to improper learning complexity"

Connected through hyperedge HE-8 (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"
    - "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"
    - "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)