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: "learning to assign orientations to feature points"
  Abstract: We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the tedious and almost impossible task of finding a target orientation to learn, we propose to use Siamese networks which implicitly find the optimal orientations during training. We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. This novel activation performs better for our task. We validate the effectiveness of our method extensively with four existing datasets, including two non-planar datasets, as well as our own dataset. We show that we outperform the state-of-the-art without the need of retraining for each dataset.

Connected through hyperedge HE-1 (induced by source paper "learning multiview 3d point cloud registration"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "efficient and robust registration on the 3d special euclidean group"
    - "3d local features for direct pairwise registration"
    - "deep closest point learning representations for point cloud registration"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "latent ransac"
    - "deepmapping unsupervised map estimation from multiple point clouds"

Connected through hyperedge HE-2 (induced by source paper "single stage 6d object pose estimation"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "neural guided ransac learning where to sample model hypotheses"
    - "dpod 6d pose object detector and refiner"
    - "backpropagation friendly eigendecomposition"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "lf net learning local features from images"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "from handcrafted to deep local features"):
  with members:
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-4 (induced by source paper "contextdesc local descriptor augmentation with cross modality context"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "matchable image retrieval by learning from surface reconstruction"
    - "fix your classifier the marginal value of training the last weight layer"
    - "local descriptors optimized for average precision"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "repeatability is not enough learning affine regions via discriminability"

Connected through hyperedge HE-5 (induced by source paper "key net keypoint detection by handcrafted and learned cnn filters"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "lf net learning local features from images"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "repeatability is not enough learning affine regions via discriminability"
    - "toward geometric deep slam"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

Connected through hyperedge HE-6 (induced by source paper "linearized multi sampling for differentiable image transformation"):
  with members:
    - "lf net learning local features from images"
    - "path aggregation network for instance segmentation"
    - "hierarchical spatial transformer network"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "controllable top down feature transformer"
    - "polar transformer networks"
    - "repeatability is not enough learning affine regions via discriminability"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "rf net an end to end image matching network based on receptive field"):
  with members:
    - "lf net learning local features from images"
    - "local descriptors optimized for average precision"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "instance normalization the missing ingredient for fast stylization"
    - "quad networks unsupervised learning to rank for interest point detection"

Connected through hyperedge HE-8 (induced by source paper "glampoints greedily learned accurate match points"):
  with members:
    - "lf net learning local features from images"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "in the saddle chasing fast and repeatable features"
    - "u net convolutional networks for biomedical image segmentation"
    - "tilde a temporally invariant learned detector"
    - "descriptor matching with convolutional neural networks a comparison to sift"

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)