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: "hdm net monocular non rigid 3d reconstruction with learned deformation model"
  Abstract: Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision. Current techniques either require dense correspondences and rely on motion and deformation cues, or assume a highly accurate reconstruction (referred to as a template) of at least a single frame given in advance and operate in the manner of non-rigid tracking. Accurate computation of dense point tracks often requires multiple frames and might be computationally expensive. Availability of a template is a very strong prior which restricts system operation to a pre-defined environment and scenarios. In this work, we propose a new hybrid approach for monocular non-rigid reconstruction which we call Hybrid Deformation Model Network (HDM-Net). In our approach, deformation model is learned by a deep neural network, with a combination of domain-specific loss functions. We train the network with multiple states of a non-rigidly deforming structure with a known shape at rest. HDM-Net learns different reconstruction cues including texture-dependent surface deformations, shading and contours. We show generalisability of HDM-Net to states not presented in the training dataset, with unseen textures and under new illumination conditions. Experiments with noisy data and a comparison with other methods demonstrate robustness and accuracy of the proposed approach and suggest possible application scenarios of the new technique in interventional diagnostics and augmented reality.

Connected through hyperedge HE-1 (induced by source paper "deep shape from template wide baseline dense and fast registration and deformable reconstruction from a single image"):
  with members:
    - "geometry aware network for non rigid shape prediction from a single view"
    - "densepose dense human pose estimation in the wild"
    - "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic 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.CV(Computer Vision and Pattern Recognition)