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: "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
  Abstract: Auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learning of global structure by global shape reconstruction, while ignoring the learning of local structures. To resolve this issue, we propose Local-to-Global auto-encoder (L2G-AE) to simultaneously learn the local and global structure of point clouds by local to global reconstruction. Specifically, L2G-AE employs an encoder to encode the geometry information of multiple scales in a local region at the same time. In addition, we introduce a novel hierarchical self-attention mechanism to highlight the important points, scales and regions at different levels in the information aggregation of the encoder. Simultaneously, L2G-AE employs a recurrent neural network (RNN) as decoder to reconstruct a sequence of scales in a local region, based on which the global point cloud is incrementally reconstructed. Our outperforming results in shape classification, retrieval and upsampling show that L2G-AE can understand point clouds better than state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "texture fields learning texture representations in function space"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "what do single view 3d reconstruction networks learn"

Connected through hyperedge HE-2 (induced by source paper "geometric back projection network for point cloud classification"):
  with members:
    - "hierarchical back projection network for image super resolution"
    - "pyramnet point cloud pyramid attention network and graph embedding module for classification and segmentation"
    - "point attention network for semantic segmentation of 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "kpconv flexible and deformable convolution for point clouds"
    - "real image denoising with feature attention"
    - "pu gan a point cloud upsampling adversarial network"

Connected through hyperedge HE-3 (induced by source paper "lrc net learning discriminative features on point clouds by encodinglocal region contexts"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "point2spatialcapsule aggregating features and spatial relationships of local regions on point clouds using spatial aware capsules"
    - "relation shape convolutional neural network for point cloud analysis"

Connected through hyperedge HE-4 (induced by source paper "non local part aware point cloud denoising"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "pu gan a point cloud upsampling adversarial network"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-5 (induced by source paper "graphter unsupervised learning of graph transformation equivariant representations via auto encoding node wise transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "relation shape convolutional neural network for point cloud analysis"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "a graph cnn for 3d point cloud classification"

Connected through hyperedge HE-6 (induced by source paper "fast low rank metric learning for large scale and high dimensional data"):
  with members:
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "online learning with an unknown fairness metric"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "opml a one pass closed form solution for online metric learning"

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)