human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "pointaugment an auto augmentation framework for point cloud classification"):
  Abstract: We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier. Moreover, we formulate a learnable point augmentation function with a shape-wise transformation and a point-wise displacement, and carefully design loss functions to adopt the augmented samples based on the learning progress of the classifier. Extensive experiments also confirm PointAugment's effectiveness and robustness to improve the performance of various networks on shape classification and retrieval.

Includes members:
  Member "std sparse to dense 3d object detector for point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "multi level semantic feature augmentation for one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification")
  Member "interpolated convolutional networks for 3d point cloud understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review deep learning on 3d point clouds")
  Member "densepoint learning densely contextual representation for efficient point cloud processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "efficient learning on point clouds with basis point sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "kpconv flexible and deformable convolution for point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "fast autoaugment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "shellnet efficient point cloud convolutional neural networks using concentric shells statistics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review deep learning on 3d point clouds")

Question: Please tell me which class the source paper 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)