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 enabled simulated annealing for topology optimization"):
  Abstract: Topology optimization by distributing materials in a domain requires stochastic optimizers to solve highly complicated problems. However, solving such problems requires millions of finite element calculations with hundreds of design variables or more involved , whose computational cost is huge and often unacceptable. To speed up computation, here we report a method to integrate deep learning into stochastic optimization algorithm. A Deep Neural Network (DNN) learns and substitutes the objective function by forming a loop with Generative Simulated Annealing (GSA). In each iteration, GSA uses DNN to evaluate the objective function to obtain an optimized solution, based on which new training data are generated; thus, DNN enhances its accuracy and GSA could accordingly improve its solution in next iteration until convergence. Our algorithm was tested by compliance minimization problems and reduced computational time by over two orders of magnitude. This approach sheds light on solving large multi-dimensional optimization problems.

Includes members:
  Member "a novel topology optimization approach using conditional deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "machine learning accelerated topology optimization of nonlinear structures")
  Member "deep learning for determining a near optimal topological design without any iteration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topologygan topology optimization using generative adversarial networks based on physical fields over the initial domain")
  Member "deep generative design integration of topology optimization and generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "topologygan topology optimization using generative adversarial networks based on physical fields over the initial domain")
  Member "3d topology optimization using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning accelerated topology optimization of nonlinear structures")
  Member "neural networks for topology optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.CE(Computational Engineering, Finance, and Science)