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 "decision trees for decision making under the predict then optimize framework"):
  Abstract: We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A natural loss function in this framework is to measure the suboptimality of the decisions induced by the predicted input parameters, as opposed to measuring loss using input parameter prediction error. This natural loss function is known in the literature as the Smart Predict-then-Optimize (SPO) loss, and we propose a tractable methodology called SPO Trees (SPOTs) for training decision trees under this loss. SPOTs benefit from the interpretability of decision trees, providing an interpretable segmentation of contextual features into groups with distinct optimal solutions to the optimization problem of interest. We conduct several numerical experiments on synthetic and real data including the prediction of travel times for shortest path problems and predicting click probabilities for news article recommendation. We demonstrate on these datasets that SPOTs simultaneously provide higher quality decisions and significantly lower model complexity than other machine learning approaches (e.g., CART) trained to minimize prediction error.

Includes members:
  Member "learning optimal and fair decision trees for non discriminative decision making":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning fair rule lists")
  Member "smart predict and optimize for hard combinatorial optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiation of blackbox combinatorial solvers")
  Member "optimal sparse decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "suod a scalable unsupervised outlier detection framework")
  Member "generalization bounds in the predict then optimize framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end learning and optimization on graphs")
  Member "end to end learning and optimization on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentiation of blackbox combinatorial solvers")
  Member "melding the data decisions pipeline decision focused learning for combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ai for social impact learning and planning in the data to deployment pipeline")
  Member "task based end to end model learning in stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "a practical method for solving contextual bandit problems using decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nonparametric pricing analytics with customer covariates")

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)