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 "genetic algorithm based multi objective optimization of solidification in die casting using deep neural network as surrogate model"):
  Abstract: In this paper, a novel strategy of multi-objective optimization of die casting is presented. The cooling of molten metal inside the mold is achieved by passing a coolant, typically water through the cooling lines in the die. Depending on the cooling line location, coolant flow rate and die geometry, nonuniform temperatures are imposed on the molten metal at the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. A finite volume based numerical solver is used to correlate the inputs to outputs. The objective of this research is to estimate the initial and wall temperatures so as to optimize the product quality. The non-dominated sorting genetic algorithm (NSGA--II) which is popular for solving multi-objective optimization problems is used. The number of function evaluations required for NSGA--II can be of the order of millions and hence, the finite volume solver cannot be used directly for optimization. Thus, a neural network trained using the results from the numerical solver is used as a surrogate model. Simplified versions of the actual problem are designed to verify results of the genetic algorithm. An innovative local sensitivity based approach is used to rank the final Pareto optimal solutions and choose a single best design.

Includes members:
  Member "finite volume simulation framework for die casting with uncertainty quantification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimization of solidification in die casting using numerical simulations and machine learning")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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)