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: "kornia an open source differentiable computer vision library for pytorch"
  Abstract: This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.

Connected through hyperedge HE-1 (induced by source paper "deltra deep light transport for projector camera systems"):
  with members:
    - "pro cam ssfm projector camera system for structure and spectral reflectance from motion"
    - "compennet end to end full projector compensation"
    - "self calibrating deep photometric stereo networks"
    - "end to end projector photometric compensation"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "a single shot per pose camera projector calibration system for imperfect planar targets"
    - "inverserendernet learning single image inverse rendering"
    - "deep learning the latent space of light transport"

Connected through hyperedge HE-2 (induced by source paper "kaolin a pytorch library for accelerating 3d deep learning research"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-3 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "cubeslam monocular 3 d object slam"
    - "generalized inner loop meta learning"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "constructing category specific models for monocular object slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-4 (induced by source paper "faster autoaugment learning augmentation strategies using backpropagation"):
  with members:
    - "fast autoaugment"
    - "mixmatch a holistic approach to semi supervised learning"
    - "pytorch an imperative style high performance deep learning library"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "mixup beyond empirical risk minimization"
    - "improved training of wasserstein gans"

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)