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: "deep rbfnet point cloud feature learning using radial basis functions"
  Abstract: Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions. However, existing methods rely heavily on fully connected layers, which introduce a significant amount of parameters, making the network harder to train and prone to overfitting problems. In this paper, we propose a simple yet effective framework for point set feature learning by leveraging a nonlinear activation layer encoded by Radial Basis Function (RBF) kernels. Unlike PointNet variants, that fail to recognize local point patterns, our approach explicitly models the spatial distribution of point clouds by aggregating features from sparsely distributed RBF kernels. A typical RBF kernel, e.g. Gaussian function, naturally penalizes long-distance response and is only activated by neighboring points. Such localized response generates highly discriminative features given different point distributions. In addition, our framework allows the joint optimization of kernel distribution and its receptive field, automatically evolving kernel configurations in an end-to-end manner. We demonstrate that the proposed network with a single RBF layer can outperform the state-of-the-art Pointnet++ in terms of classification accuracy for 3D object recognition tasks. Moreover, the introduction of nonlinear mappings significantly reduces the number of network parameters and computational cost, enabling significantly faster training and a deployable point cloud recognition solution on portable devices with limited resources.

Connected through hyperedge HE-1 (induced by source paper "deep mesh reconstruction from single rgb images via topology modification networks"):
  with members:
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "learning category specific mesh reconstruction from image collections"
    - "bodynet volumetric inference of 3d human body shapes"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "siclope silhouette based clothed people"

Connected through hyperedge HE-2 (induced by source paper "latticenet fast point cloud segmentation using permutohedral lattices"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "group normalization"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "real time progressive 3d semantic segmentation for indoor scene"

Connected through hyperedge HE-3 (induced by source paper "deeplabnet end to end learning of deep radial basis networks with fully learnable basis functions"):
  with members:
    - "adaptive blending units trainable activation functions for deep neural networks"
    - "deep rbf networks revisited robust classification with rejection"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "squeeze and excitation networks"
    - "searching for activation functions"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "geonet deep geodesic networks for point cloud analysis"):
  with members:
    - "dense depth posterior ddp from single image and sparse range"
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "conditional prior networks for optical flow"
    - "geo supervised visual depth prediction"
    - "pcpnet learning local shape properties from raw point clouds"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-5 (induced by source paper "multi modal 3d shape reconstruction under calibration uncertainty using parametric level set methods"):
  with members:
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "a parametric level set method for partially discrete tomography"
    - "salt reconstruction in full waveform inversion with a parametric level set method"
    - "full waveform inversion guided by travel time tomography"
    - "jinv a flexible julia package for pde parameter estimation"
    - "julia a fresh approach to numerical computing"
    - "surface reconstruction from scattered point via rbf interpolation on gpu"

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)