human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "hypothesis testing in the high privacy limit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy with estimation guarantees")
  Member "iterative orthogonal feature projection for diagnosing bias in black box models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "metamorphic testing of a deep learning based forecaster")
  Member "discrimination in online ad delivery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "getting fairness right towards a toolbox for practitioners")
  Member "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "evaluation of marton s inner bound for the general broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "strong secrecy for cooperative broadcast channels")
  Member "evaluation of marton s inner bound for the general broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "strong secrecy for cooperative broadcast channels")

Question: Please tell me which class the source paper 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)