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: "inverse compositional spatial transformer networks"
  Abstract: In this paper, we establish a theoretical connection between the classical Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer Networks (STNs). STNs are of interest to the vision and learning communities due to their natural ability to combine alignment and classification within the same theoretical framework. Inspired by the Inverse Compositional (IC) variant of the LK algorithm, we present Inverse Compositional Spatial Transformer Networks (IC-STNs). We demonstrate that IC-STNs can achieve better performance than conventional STNs with less model capacity; in particular, we show superior performance in pure image alignment tasks as well as joint alignment/classification problems on real-world problems.

Connected through hyperedge HE-1 (induced by source paper "deepim deep iterative matching for 6d pose estimation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geometric loss functions for camera pose regression with deep learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-2 (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"
    - "linearized multi sampling for differentiable image transformation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of camera pose with compositional re estimation"):
  with members:
    - "on the convergence of adam and beyond"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "megadepth learning single view depth prediction from internet photos"

Connected through hyperedge HE-4 (induced by source paper "learning from unlabelled videos using contrastive predictive neural 3d mapping"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning spatial common sense with geometry aware recurrent networks"
    - "learning to decompose and disentangle representations for video prediction"
    - "representation learning with contrastive predictive coding"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-5 (induced by source paper "the problems with using stns to align cnn feature maps"):
  with members:
    - "spatial transformer networks"

Connected through hyperedge HE-6 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "transformable bottleneck networks"):
  with members:
    - "3dn 3d deformation network"
    - "visual object networks image generation with disentangled 3d representation"
    - "geometry aware deep network for single image novel view synthesis"
    - "layer structured 3d scene inference via view synthesis"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "megadepth learning single view depth prediction from internet photos"

Connected through hyperedge HE-8 (induced by source paper "generalized feedback loop for joint hand object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "dynamics based 3d skeletal hand tracking"

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)