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: "spectral algorithms for computing fair support vector machines"
  Abstract: Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i.e., age, gender, or race). So it is important to understand how to design classifiers and scores that prevent discrimination in predictions. This paper develops computationally tractable algorithms for designing accurate but fair support vector machines (SVM's). Our approach imposes a constraint on the covariance matrices conditioned on each protected class, which leads to a nonconvex quadratic constraint in the SVM formulation. We develop iterative algorithms to compute fair linear and kernel SVM's, which solve a sequence of relaxations constructed using a spectral decomposition of the nonconvex constraint. Its effectiveness in achieving high prediction accuracy while ensuring fairness is shown through numerical experiments on several data sets.

Connected through hyperedge HE-1 (induced by source paper "fairness risk measures"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "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"
    - "equality of opportunity in supervised learning"
    - "fairness through awareness"

Connected through hyperedge HE-2 (induced by source paper "exact guarantees on the absence of spurious local minima for non negative rank 1 robust principal component analysis"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "how much restricted isometry is needed in nonconvex matrix recovery"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "global optimality in low rank matrix optimization"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-3 (induced by source paper "convex formulations for fair principal component analysis"):
  with members:
    - "fair clustering through fairlets"
    - "mitigating unwanted biases with adversarial learning"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "equality of opportunity in supervised learning"
    - "on the relation between accuracy and fairness in binary classification"
    - "fairness through awareness"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-4 (induced by source paper "fairness aware classification criterion convexity and bounds"):
  with members:
    - "on fairness and calibration"
    - "achieving non discrimination in prediction"
    - "equality of opportunity in supervised learning"
    - "achieving non discrimination in data release"
    - "inherent trade offs in the fair determination of risk scores"
    - "a causal framework for discovering and removing direct and indirect discrimination"
    - "satisfying real world goals with dataset constraints"

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)