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 "fairness through computationally bounded awareness"):
  Abstract: We study the problem of fair classification within the versatile framework of Dwork et al. [ITCS 2012], which assumes the existence of a metric that measures similarity between pairs of individuals. Unlike previous works on metric-based fairness, we do not assume that the entire metric is known to the learning algorithm. Instead, we study the setting where a learning algorithm can query this metric a bounded number of times to ascertain similarities between particular pairs of individuals. For example, the queries might be answered by a panel of specialists spanning social scientists, statisticians, demographers, and ethicists. #R##N#We propose "metric multifairness," a new definition of fairness that is parameterized by a similarity metric $\delta$ on pairs of individuals and a collection ${\cal C}$ of"comparison sets" over pairs of individuals. One way to view this collection is as the family of comparisons that can be expressed within some computational bound. With this interpretation, metric multifairnesss loosely guarantees that similar subpopulations are treated similarly, as long as these subpopulations can be identified within this bound. In particular, metric multifairness implies that a rich class of subpopulations are protected from a multitude of discriminatory behaviors. #R##N#We provide a general-purpose framework for learning a metric multifair hypothesis that achieves near-optimal loss from a small number of random samples from the metric $\delta$. We study the sample complexity and time complexity of learning a metric multifair hypothesis (providing rather tight upper and lower bounds) by connecting it to the task of learning the class ${\cal C}$. In particular, if the class ${\cal C}$ admits an efficient agnostic learner, then we can learn such a metric multifair hypothesis efficiently.

Includes members:
  Member "multiaccuracy black box post processing for fairness in classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "online learning with an unknown fairness metric":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "on fairness and calibration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "preventing fairness gerrymandering auditing and learning for subgroup fairness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "fairness through awareness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithmic fairness")
  Member "agnostic learning of monomials by halfspaces is hard":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time")

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.LG(Machine Learning)