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: "unleashing linear optimizers for group fair learning and optimization"
  Abstract: Most systems and learning algorithms optimize average performance or average loss -- one reason being computational complexity. However, many objectives of practical interest are more complex than simply average loss. This arises, for example, when balancing performance or loss with fairness across people. We prove that, from a computational perspective, optimizing arbitrary objectives that take into account performance over a small number of groups is not significantly harder to optimize than average performance. Our main result is a polynomial-time reduction that uses a linear optimizer to optimize an arbitrary (Lipschitz continuous) function of performance over a (constant) number of possibly-overlapping groups. This includes fairness objectives over small numbers of groups, and we further point out that other existing notions of fairness such as individual fairness can be cast as convex optimization and hence more standard convex techniques can be used. Beyond learning, our approach applies to multi-objective optimization, more generally.

Connected through hyperedge HE-1 (induced by source paper "optimizing black box metrics with adaptive surrogates"):
  with members:
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "optimizing generalized rate metrics through game equilibrium"
    - "learning surrogate losses"
    - "learning to teach with dynamic loss functions"
    - "constrained classification and ranking via quantiles"
    - "learning to reweight examples for robust deep learning"
    - "online and stochastic gradient methods for non decomposable loss functions"
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-2 (induced by source paper "r enyi fair inference"):
  with members:
    - "fair logistic regression an adversarial perspective"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "tunable measures for information leakage and applications to privacy utility tradeoffs"
    - "fair clustering through fairlets"
    - "on the convergence and robustness of training gans with regularized optimal transport"

Connected through hyperedge HE-3 (induced by source paper "the cost of a reductions approach to private fair optimization"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "privacy amplification by iteration"
    - "a reductions approach to fair classification"
    - "multi objective bandits optimizing the generalized gini index"
    - "equality of opportunity in supervised learning"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "between pure and approximate differential privacy"

Connected through hyperedge HE-4 (induced by source paper "fairness risk measures"):
  with members:
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "a reductions approach to fair classification"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "fairness in supervised learning an information theoretic approach"
    - "spectral algorithms for computing fair support vector machines"
    - "equality of opportunity in supervised learning"
    - "fairness through awareness"

Connected through hyperedge HE-5 (induced by source paper "fair regression quantitative definitions and reduction based algorithms"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a reductions approach to fair classification"
    - "xgboost a scalable tree boosting system"
    - "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:
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "how to use heuristics for differential privacy"):
  with members:
    - "model agnostic private learning via stability"
    - "property testing for differential privacy"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "online learning via differential privacy"
    - "adaptive learning with robust generalization guarantees"
    - "efficient algorithms for adversarial contextual learning"
    - "differentially private release and learning of threshold functions"

Connected through hyperedge HE-8 (induced by source paper "a reductions approach to fair classification"):
  with members:
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "fairness through awareness"

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)