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: "see the difference direct pre image reconstruction and pose estimation by differentiating hog"
  Abstract: The Histogram of Oriented Gradient (HOG) descriptor has led to many advances in computer vision over the last decade and is still part of many state of the art approaches. We realize that the associated feature computation is piecewise differentiable and therefore many pipelines which build on HOG can be made differentiable. This lends to advanced introspection as well as opportunities for end-to-end optimization. We present our implementation of $\nabla$HOG based on the auto-differentiation toolbox Chumpy and show applications to pre-image visualization and pose estimation which extends the existing differentiable renderer OpenDR pipeline. Both applications improve on the respective state-of-the-art HOG approaches.

Connected through hyperedge HE-1 (induced by source paper "discriminative structural graph classification"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "on the universality of invariant networks"
    - "fast graph representation learning with pytorch geometric"
    - "on the transferability of spectral graph filters"
    - "learnable histogram statistical context features for deep neural networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "learnable histogram statistical context features for deep neural networks"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "human pose estimation with iterative error feedback"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "novel views of objects from a single image"):
  with members:
    - "vconv dae deep volumetric shape learning without object labels"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"
    - "shapenet an information rich 3d model repository"
    - "deep reflectance maps"
    - "fast r cnn"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "learning deep object detectors from 3d models"

Connected through hyperedge HE-4 (induced by source paper "model based deep hand pose estimation"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "depth based hand pose estimation methods data and challenges"
    - "fast and robust hand tracking using detection guided optimization"
    - "hybrid one shot 3d hand pose estimation by exploiting uncertainties"
    - "hands deep in deep learning for hand pose estimation"
    - "caffe convolutional architecture for fast feature embedding"

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)