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 fairness of randomized trials for recommendation with heterogeneous demographics and beyond"):
  Abstract: Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk. A general approach to correct MNAR bias is performing small Randomized Controlled Trials (RCTs), where an additional uniform policy is employed to randomly assign items to each user. In this work, we concentrate on the fairness of RCTs under both homogeneous and heterogeneous demographics, especially analyzing the bias for the least favorable group on the latter setting. Considering RCTs' limitations, we propose a novel Counterfactual Robust Risk Minimization (CRRM) framework, which is totally free of expensive RCTs, and derive its theoretical generalization error bound. At last, empirical experiments are performed on synthetic tasks and real-world data sets, substantiating our method's superiority both in fairness and generalization.

Includes members:
  Member "average individual fairness algorithms generalization and experiments":
    also connected through other hyperedges:
      (none)
  Member "fairness in recommendation ranking through pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "policy learning for fairness in ranking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "less is better unweighted data subsampling via influence function":
    also connected through other hyperedges:
      (none)
  Member "delayed impact of fair machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "learning optimal fair policies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on bias and fairness in machine learning")
  Member "causal embeddings for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "eliminating bias in recommender systems via pseudo labeling")
  Member "preventing fairness gerrymandering auditing and learning for subgroup fairness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "to split or not to split the impact of disparate treatment in classification")

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)