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: "designing fair ranking schemes"
  Abstract: Items from a database are often ranked based on a combination of multiple criteria. A user may have the flexibility to accept combinations that weigh these criteria differently, within limits. On the other hand, this choice of weights can greatly affect the fairness of the produced ranking. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairness. #R##N#We consider ranking functions that compute the score of each item as a weighted sum of (numeric) attribute values, and then sort items on their score. Each ranking function can be expressed as a vector of weights, or as a point in a multi-dimensional space. For a broad range of fairness criteria, we show how to efficiently identify regions in this space that satisfy these criteria. Using this identification method, our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and, if it does not, to suggest the smallest modification that does. We develop user-controllable approximation that and indexing techniques that are applied during preprocessing, and support sub-second response times during the online phase. Our extensive experiments on real datasets demonstrate that our methods are able to find solutions that satisfy fairness criteria effectively and efficiently.

Connected through hyperedge HE-1 (induced by source paper "fairsight visual analytics for fairness in decision making"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "a nutritional label for rankings"
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-2 (induced by source paper "policy learning for fairness in ranking"):
  with members:
    - "intervention harvesting for context dependent examination bias estimation"
    - "estimating position bias without intrusive interventions"
    - "equity of attention amortizing individual fairness in rankings"
    - "reducing disparate exposure in ranking a learning to rank approach"
    - "fairness of exposure in rankings"
    - "ranking with fairness constraints"
    - "learning non discriminatory predictors"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-3 (induced by source paper "consequential ranking algorithms and long term welfare"):
  with members:
    - "policy learning for fairness in ranking"
    - "delayed impact of fair machine learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "a short term intervention for long term fairness in the labor market"
    - "equity of attention amortizing individual fairness in rankings"
    - "from fair decision making to social equality"
    - "fairness of exposure in rankings"
    - "deconvolving feedback loops in recommender systems"

Connected through hyperedge HE-4 (induced by source paper "using image fairness representations in diversity based re ranking for recommendations"):
  with members:
    - "fairness of exposure in rankings"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "billion scale similarity search with gpus"
    - "visual search at ebay"
    - "ranking with fairness constraints"
    - "fa ir a fair top k ranking algorithm"
    - "measuring fairness in ranked outputs"
    - "visual search at pinterest"

Connected through hyperedge HE-5 (induced by source paper "fairness of exposure in rankings"):
  with members:
    - "equity of attention amortizing individual fairness in rankings"
    - "ranking with fairness constraints"
    - "learning non discriminatory predictors"
    - "fa ir a fair top k ranking algorithm"
    - "unbiased learning to rank with biased feedback"
    - "equality of opportunity in supervised learning"
    - "measuring fairness in ranked outputs"
    - "recommendations as treatments debiasing learning and evaluation"

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.DB(Databases)