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: "convex formulations for fair principal component analysis"
  Abstract: Though there is a growing body of literature on fairness for supervised learning, the problem of incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first present a definition of fairness for dimensionality reduction, and our definition can be interpreted as saying that a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs (SDP's), and we demonstrate the effectiveness of our formulations using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.

Connected through hyperedge HE-1 (induced by source paper "fair principal component analysis and filter design"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "efficient fair principal component analysis"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "the price of fair pca one extra dimension"
    - "global optimality in low rank matrix optimization"
    - "robust computation of linear models by convex relaxation"

Connected through hyperedge HE-2 (induced by source paper "fairness in representation quantifying stereotyping as a representational harm"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "fair algorithms for clustering"
    - "understanding the origins of bias in word embeddings"
    - "fair clustering through fairlets"
    - "the price of fair pca one extra dimension"
    - "learning adversarially fair and transferable representations"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-3 (induced by source paper "near neighbor who is the fairest of them all"):
  with members:
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "independent range sampling revisited again"
    - "fair near neighbor search independent range sampling in high dimensions"
    - "fair clustering through fairlets"
    - "fair algorithms for learning in allocation problems"
    - "a reductions approach to fair classification"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-4 (induced by source paper "fairness in clustering with multiple sensitive attributes"):
  with members:
    - "proportionally fair clustering"
    - "principal fairness removing bias via projections"
    - "fair algorithms for clustering"
    - "towards fair deep clustering with multi state protected variables"
    - "scalable fair clustering"
    - "clustering with fairness constraints a flexible and scalable approach"
    - "clustering without over representation"
    - "privacy preserving clustering with constraints"

Connected through hyperedge HE-5 (induced by source paper "efficient fair principal component analysis"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "downstream effects of affirmative action"
    - "the price of fair pca one extra dimension"
    - "fairness under composition"
    - "a reductions approach to fair classification"
    - "eliminating latent discrimination train then mask"
    - "on fairness and calibration"

Connected through hyperedge HE-6 (induced by source paper "learning fair and interpretable representations via linear orthogonalization"):
  with members:
    - "unified adversarial invariance"
    - "50 years of test un fairness lessons for machine learning"
    - "on the fairness of disentangled representations"
    - "the price of fair pca one extra dimension"
    - "the frontiers of fairness in machine learning"
    - "a convex framework for fair regression"
    - "controllable invariance through adversarial feature learning"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-7 (induced by source paper "a distributed fair machine learning framework with private demographic data protection"):
  with members:
    - "envy free classification"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the price of fair pca one extra dimension"
    - "provably fair representations"
    - "equality of opportunity in supervised learning"
    - "a confidence based approach for balancing fairness and accuracy"
    - "fairness through awareness"

Connected through hyperedge HE-8 (induced by source paper "fair kernel regression via fair feature embedding in kernel space"):
  with members:
    - "the price of fair pca one extra dimension"
    - "provably fair representations"
    - "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)