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: "toward controlling discrimination in online ad auctions"
  Abstract: Online advertising platforms are thriving due to the customizable audiences they offer advertisers. However, recent studies show that advertisements can be discriminatory with respect to the gender or race of the audience that sees the ad, and may inadvertently cross ethical and/or legal boundaries. To prevent this, we propose a constrained ad auction framework that maximizes the platform's revenue conditioned on ensuring that the audience seeing an advertiser's ad is distributed appropriately across sensitive types such as gender or race. Building upon Myerson's classic work, we first present an optimal auction mechanism for a large class of fairness constraints. Finding the parameters of this optimal auction, however, turns out to be a non-convex problem. We show that this non-convex problem can be reformulated as a more structured non-convex problem with no saddle points or local-maxima; this allows us to develop a gradient-descent-based algorithm to solve it. Our empirical results on the A1 Yahoo! dataset demonstrate that our algorithm can obtain uniform coverage across different user types for each advertiser at a minor loss to the revenue of the platform, and a small change to the size of the audience each advertiser reaches.

Connected through hyperedge HE-1 (induced by source paper "interventions for ranking in the presence of implicit bias"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "coresets for clustering with fairness constraints"
    - "pairwise fairness for ranking and regression"
    - "fair and diverse dpp based data summarization"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "fairness of exposure in rankings"
    - "multiwinner voting with fairness constraints"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-2 (induced by source paper "stable and fair classification"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "generalization bounds for uniformly stable algorithms"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "certified defenses for data poisoning attacks"
    - "mathematics of deep learning"

Connected through hyperedge HE-3 (induced by source paper "coresets for clustering with fairness constraints"):
  with members:
    - "principal fairness removing bias via projections"
    - "scalable fair clustering"
    - "clustering without over representation"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair and diverse dpp based data summarization"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fair clustering through fairlets"

Connected through hyperedge HE-4 (induced by source paper "ai and holistic review informing human reading in college admissions"):
  with members:
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "datasheets for datasets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "distributed representations of words and phrases and their compositionality"
    - "l2 regularization for learning kernels"
    - "nltk the natural language toolkit"

Connected through hyperedge HE-5 (induced by source paper "multi category fairness in sponsored search auctions"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "balanced news using constrained bandit based personalization"
    - "a new system wide diversity measure for recommendations with efficient algorithms"
    - "fairness under composition"
    - "ranking with fairness constraints"
    - "fairness through awareness"

Connected through hyperedge HE-6 (induced by source paper "bidding strategies with gender nondiscrimination constraints for online ad auctions"):
  with members:
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"

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.GT(Computer Science and Game Theory)