human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "image based reconstruction for a 3d pfhs heat transfer problem by reconnn"
  Abstract: The heat transfer performance of Plate Fin Heat Sink (PFHS) has been investigated experimentally and extensively. Commonly, the objective function of PFHS design is based on the responses of simulations. Compared with existing studies, the purpose of this work is to transfer from image-based model to analysis-based model for heat sink designs. It means that the sequential optimization should be based on images instead of responses. Therefore, an image-based reconstruction model of a heat transfer process for a 3D-PFHS is established. Unlike image recognition, such procedure cannot be implemented by existing recognition algorithms (e.g. Convolutional Neural Network) directly. Therefore, a Reconstructive Neural Network (ReConNN), integrated supervised learning and unsupervised learning techniques, is suggested. According to the experimental results, the heat transfer process can be observed more detailed and clearly, and the reconstructed results are meaningful for the further optimizations.

Connected through hyperedge HE-1 (induced by source paper "image based reconstruction for the impact problems by using dpnns"):
  with members:
    - "deep learning for determining a near optimal topological design without any iteration"
    - "exploring the 3d architectures of deep material network in data driven multiscale mechanics"
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"
    - "3d topology optimization using convolutional neural networks"
    - "how to control the crack to propagate along the specified path feasibly"
    - "neural networks for topology optimization"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "image based reconstruction for strong nonlinear transient problems by using an enhanced reconnn"):
  with members:
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"
    - "how to control the crack to propagate along the specified path feasibly"
    - "neural networks for topology optimization"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Question: Please tell me which class the center node 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)