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 "deep learning powered iterative combinatorial auctions"):
  Abstract: In this paper, we study the design of deep learning-powered iterative combinatorial auctions (ICAs). We build on the work by Brero et al. (2018), who have successfully integrated support vector regression (SVR) into the preference elicitation algorithm of an ICA. However, their SVR-based approach also has its limitations because the algorithm requires solving the auction's winner determination problem (WDP) given predicted value functions. With expressive kernels (like gaussian, exponential or high degree polynomial kernels), the WDP cannot be solved for large domains. While linear or low-degree polynomial kernels have better computational scalability, these kernels have limited expressiveness. In this work, we address these shortcomings by using deep neural networks (DNNs) instead of SVRs for learning bidders' valuation functions. Our main contribution is to show that the resulting maximization step of DNNs consisting of rectified linear units as activation functions can always be reformulated into a mixed integer linear program (MILP). Preliminary simulation results indicate that even two-hidden-layer-fully-connected DNNs with a small number of hidden units lead to higher economic efficiency than kernelized SVRs with a comparable or even smaller runtime.

Includes members:
  Member "machine learning powered iterative combinatorial auctions":
    also connected through other hyperedges:
      (none)
  Member "fast iterative combinatorial auctions via bayesian learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrete signal processing with set functions")
  Member "deep neural networks as 0 1 mixed integer linear programs a feasibility study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "randomized smoothing of all shapes and sizes")
  Member "evaluating robustness of neural networks with mixed integer programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression of deep neural networks")
  Member "a bayesian clearing mechanism for combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning powered iterative combinatorial auctions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.GT(Computer Science and Game Theory)