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: "lightweight structure design under force location uncertainty"
  Abstract: We introduce a lightweight structure optimization approach for problems in which there is uncertainty in the force locations. Such uncertainty may arise due to force contact locations that change during use or are simply unknown a priori. Given an input 3D model, regions on its boundary where arbitrary normal forces may make contact, and a total force-magnitude budget, our algorithm generates a minimum weight 3D structure that withstands any force configuration capped by the budget. Our approach works by repeatedly finding the most critical force configuration and altering the internal structure accordingly. A key issue, however, is that the critical force configuration changes as the structure evolves, resulting in a significant computational challenge. To address this, we propose an efficient critical instant analysis approach. Combined with a reduced order formulation, our method provides a practical solution to the structural optimization problem. We demonstrate our method on a variety of models and validate it with mechanical tests.

Connected through hyperedge HE-1 (induced by source paper "topologygan topology optimization using generative adversarial networks based on physical fields over the initial domain"):
  with members:
    - "deep learning for determining a near optimal topological design without any iteration"
    - "stress field prediction in cantilevered structures using convolutional neural networks"
    - "deep generative design integration of topology optimization and generative models"
    - "one shot generation of near optimal topology through theory driven machine learning"
    - "neural networks for topology optimization"
    - "squeeze and excitation networks"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "structural design using laplacian shells"):
  with members:
    - "two scale topology optimization with microstructures"
    - "from 3d models to 3d prints an overview of the processing pipeline"
    - "proportional topology optimization a new non sensitivity method for solving stress constrained and minimum compliance problems and its implementation in matlab"
    - "geodesics in heat a new approach to computing distance based on heat flow"

Connected through hyperedge HE-3 (induced by source paper "design and optimization of conforming lattice structures"):
  with members:
    - "infill optimization for additive manufacturing approaching bone like porous structures"
    - "designing volumetric truss structures"
    - "two scale topology optimization with microstructures"

Connected through hyperedge HE-4 (induced by source paper "3d topology optimization using convolutional neural networks"):
  with members:
    - "neural networks for topology optimization"

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.GR(Graphics)