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: "density adaptive point set registration"
  Abstract: Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets. We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real-world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.

Connected through hyperedge HE-1 (induced by source paper "non iterative one step solution for point set registration problem on pose estimation without correspondence"):
  with members:
    - "point set registration coherent point drift"

Connected through hyperedge HE-2 (induced by source paper "unsupervised multi task feature learning on point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "3d point capsule networks"
    - "clustering with deep learning taxonomy and new methods"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep clustering for unsupervised learning of visual features"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-3 (induced by source paper "adversarial attack and defense on point sets"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "generating 3d adversarial point clouds"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deep sets"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "3d local features for direct pairwise registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "latent ransac"
    - "bop benchmark for 6d object pose estimation"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "guaranteed outlier removal for point cloud registration with correspondences"

Connected through hyperedge HE-5 (induced by source paper "efficient and robust registration on the 3d special euclidean group"):
  with members:
    - "joint alignment of multiple point sets with batch and incremental expectation maximization"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-6 (induced by source paper "dup net denoiser and upsampler network for 3d adversarial point clouds defense"):
  with members:
    - "adversarial attack and defense on point sets"
    - "one pixel attack for fooling deep neural networks"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "realistic adversarial examples in 3d meshes"
    - "generating 3d adversarial point clouds"
    - "pixel2mesh generating 3d mesh models from single rgb images"

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)