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 "learning to optimize with hidden constraints"):
  Abstract: We consider a data-driven framework for learning to generate decisions to instances of continuous optimization problems where the feasible set varies with an instance-specific auxiliary input. We use a data set of inputs and feasible solutions, as well as an oracle of feasibility, to iteratively train two machine learning models. The first model is a binary classifier for feasibility, which then serves as a barrier function to train the second model via an interior point method. We develop theory and optimality guarantees for interior point methods when given a barrier that relaxes the feasible set, and extend these results to obtain probabilistic out-of-sample guarantees for our learning framework. Finally, we implement our method on a radiation therapy treatment planning problem to predict personalized treatments for head-and-neck cancer patients.

Includes members:
  Member "predicting solution summaries to integer linear programs under imperfect information with machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a language processing algorithm for predicting tactical solutions to an operational planning problem under uncertainty")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "the numerics of gans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "task based end to end model learning in stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "gradient descent gan optimization is locally stable":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial code learning for image generation")
  Member "unsupervised cross domain image generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "nips 2016 tutorial generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)