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: "linearized multi sampling for differentiable image transformation"
  Abstract: We propose a novel image sampling method for differentiable image transformation in deep neural networks. The sampling schemes currently used in deep learning, such as Spatial Transformer Networks, rely on bilinear interpolation, which performs poorly under severe scale changes, and more importantly, results in poor gradient propagation. This is due to their strict reliance on direct neighbors. Instead, we propose to generate random auxiliary samples in the vicinity of each pixel in the sampled image, and create a linear approximation using their intensity values. We then use this approximation as a differentiable formula for the transformed image. However, we observe that these auxiliary samples may collapse to a single pixel under severe image transformations, and propose to address it by adding constraints to the distance between the center pixel and the auxiliary samples. We demonstrate that our approach produces more representative gradients with a wider basin of convergence for image alignment, which leads to considerable performance improvements when training networks for image registration and classification tasks, particularly under large downsampling.

Connected through hyperedge HE-1 (induced by source paper "deep image spatial transformation for person image generation"):
  with members:
    - "multi channel attention selection gans for guided image to image translation"
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "dense intrinsic appearance flow for human pose transfer"
    - "structureflow image inpainting via structure aware appearance flow"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "local relation networks for image recognition"

Connected through hyperedge HE-2 (induced by source paper "synsin end to end view synthesis from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "differentiable surface splatting for point based geometry processing"
    - "neural point based graphics"
    - "the replica dataset a digital replica of indoor spaces"
    - "deepview view synthesis with learned gradient descent"
    - "neural volumes learning dynamic renderable volumes from images"
    - "semantic image synthesis with spatially adaptive normalization"
    - "mesh r cnn"

Connected through hyperedge HE-3 (induced by source paper "mist multiple instance spatial transformer networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "min entropy latent model for weakly supervised object detection"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "lf net learning local features from images"
    - "multi attention multi class constraint for fine grained image recognition"
    - "self produced guidance for weakly supervised object localization"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"

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)