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: "new variational method of grid generation with prescribed jacobian determinant and prescribed curl"
  Abstract: Adaptive grid generation is an active research topic for numer- ical solution of differential equations. In this paper, we propose a variational method which generates transformations with prescribed Jacobian determinant and curl. Then we use this transformation to achieve adaptive grid generation task, and show the importance of curl in a transformation.

Connected through hyperedge HE-1 (induced by source paper "medical image super resolution using a generative adversarial network"):
  with members:
    - "a multi channel network with image retrieval for accurate brain tissue segmentation"
    - "a strategy of mr brain tissue images suggestive annotation based on modified u net"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "automatic segmentation of mr brain images with a convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "deformable registration using average geometric transformations for brain mr images"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "geometric understanding of deep learning"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "uniqueness of transformation based on jacobian determinant and curl vector"):
  with members:
    - "a novel deformation method for higher order mesh generation"

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.CG(Computational Geometry)