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: "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning"
  Abstract: In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive attribute (e.g., race or gender). In this paper, we present an information-theoretic framework to analyze the disparate impact of a binary classification model. We view the model as a fixed channel, and quantify disparate impact as the divergence in output distributions over two groups. We then aim to find a \textit{correction function} that can be used to perturb the input distributions of each group in order to align their output distributions. We present an optimization problem that can be solved to obtain a correction function that will make the output distributions statistically indistinguishable. We derive closed-form expression for the correction function that can be used to compute it efficiently. We illustrate the use of the correction function for a recidivism prediction application derived from the ProPublica COMPAS dataset.

Connected through hyperedge HE-1 (induced by source paper "to split or not to split the impact of disparate treatment in classification"):
  with members:
    - "algorithmic fairness"
    - "recovering from biased data can fairness constraints improve accuracy"
    - "repairing without retraining avoiding disparate impact with counterfactual distributions"
    - "a framework for understanding unintended consequences of machine learning"
    - "optimized score transformation for fair classification"
    - "fairness with minimal harm a pareto optimal approach for healthcare"
    - "obfuscation via information density estimation"
    - "welfare and distributional impacts of fair classification"

Connected through hyperedge HE-2 (induced by source paper "repairing without retraining avoiding disparate impact with counterfactual distributions"):
  with members:
    - "algorithmic fairness"
    - "from soft classifiers to hard decisions how fair can we be"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "fairness testing testing software for discrimination"
    - "algorithmic decision making and the cost of fairness"
    - "proxy non discrimination in data driven systems"
    - "equality of opportunity in supervised learning"

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.IT(Information Theory)