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: "hunting for discriminatory proxies in linear regression models"
  Abstract: A machine learning model may exhibit discrimination when used to make decisions involving people. One potential cause for such outcomes is that the model uses a statistical proxy for a protected demographic attribute. In this paper we formulate a definition of proxy use for the setting of linear regression and present algorithms for detecting proxies. Our definition follows recent work on proxies in classification models, and characterizes a model's constituent behavior that: 1) correlates closely with a protected random variable, and 2) is causally influential in the overall behavior of the model. We show that proxies in linear regression models can be efficiently identified by solving a second-order cone program, and further extend this result to account for situations where the use of a certain input variable is justified as a `business necessity'. Finally, we present empirical results on two law enforcement datasets that exhibit varying degrees of racial disparity in prediction outcomes, demonstrating that proxies shed useful light on the causes of discriminatory behavior in models.

Connected through hyperedge HE-1 (induced by source paper "fliptest fairness testing via optimal transport"):
  with members:
    - "adversarial computation of optimal transport maps"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness under composition"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "approximating optimal transport with linear programs"
    - "automated test generation to detect individual discrimination in ai models"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-2 (induced by source paper "fliptest fairness auditing via optimal transport"):
  with members:
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "fairness in learning classic and contextual bandits"
    - "theano a python framework for fast computation of mathematical expressions"
    - "fairtest discovering unwarranted associations in data driven applications"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "algorithms that don t see color comparing biases in lookalike and special ad audiences"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "ad delivery algorithms the hidden arbiters of political messaging"
    - "fairness under composition"
    - "questioning the assumptions behind fairness solutions"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-4 (induced by source paper "learning fair representations for kernel models"):
  with members:
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "proxy non discrimination in data driven systems"
    - "equality of opportunity in supervised learning"
    - "censoring representations with an adversary"

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)