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 "image based reconstruction for the impact problems by using dpnns"):
  Abstract: With the improvement of the pattern recognition and feature extraction of Deep Neural Networks (DPNNs), image-based design and optimization have been widely used in multidisciplinary researches. Recently, a Reconstructive Neural Network (ReConNN) has been proposed to obtain an image-based model from an analysis-based model [1, 2], and a steady-state heat transfer of a heat sink has been successfully reconstructed. Commonly, this method is suitable to handle stable-state problems. However, it has difficulties handling nonlinear transient impact problems, due to the bottlenecks of the Deep Neural Network (DPNN). For example, nonlinear transient problems make it difficult for the Generative Adversarial Network (GAN) to generate various reasonable images. Therefore, in this study, an improved ReConNN method is proposed to address the mentioned weaknesses. Time-dependent ordered images can be generated. Furthermore, the improved method is successfully applied in impact simulation case and engineering experiment. Through the experiments, comparisons and analyses, the improved method is demonstrated to outperform the former one in terms of its accuracy, efficiency and costs.

Includes members:
  Member "deep learning for determining a near optimal topological design without any iteration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topologygan topology optimization using generative adversarial networks based on physical fields over the initial domain")
  Member "exploring the 3d architectures of deep material network in data driven multiscale mechanics":
    also connected through other hyperedges:
      (none)
  Member "automatic pavement crack detection based on structured prediction with the convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automated pavement crack segmentation using fully convolutional u net with a pretrained resnet 34 encoder")
  Member "3d topology optimization using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning accelerated topology optimization of nonlinear structures")
  Member "image based reconstruction for a 3d pfhs heat transfer problem by reconnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "image based reconstruction for strong nonlinear transient problems by using an enhanced reconnn")
  Member "how to control the crack to propagate along the specified path feasibly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image based reconstruction for strong nonlinear transient problems by using an enhanced reconnn")
  Member "neural networks for topology optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "deep reconstruction classification networks for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")

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.OH(Other Computer Science)