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: "sips succinct interest points from unsupervised inlierness probability learning"
  Abstract: A wide range of computer vision algorithms rely on identifying sparse interest points in images and establishing correspondences between them. However, only a subset of the initially identified interest points results in true correspondences (inliers). In this paper, we seek a detector that finds the minimum number of points that are likely to result in an application-dependent "sufficient" number of inliers k. To quantify this goal, we introduce the "k-succinctness" metric. Extracting a minimum number of interest points is attractive for many applications, because it can reduce computational load, memory, and data transmission. Alongside succinctness, we introduce an unsupervised training methodology for interest point detectors that is based on predicting the probability of a given pixel being an inlier. In comparison to previous learned detectors, our method requires the least amount of data pre-processing. Our detector and other state-of-the-art detectors are extensively evaluated with respect to succinctness on popular public datasets covering both indoor and outdoor scenes, and both wide and narrow baselines. In certain cases, our detector is able to obtain an equivalent amount of inliers with as little as 60% of the amount of points of other detectors. The code and trained networks are provided at https://github.com/uzh-rpg/sips2_open .

Connected through hyperedge HE-1 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lo net deep real time lidar odometry"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (induced by source paper "reinforced feature points optimizing feature detection and description for a high level task"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "elf embedded localisation of features in pre trained cnn"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "lf net learning local features from images"
    - "geodesc learning local descriptors by integrating geometry constraints"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning framework of interest point via properties optimization"):
  with members:
    - "lf net learning local features from images"
    - "sips unsupervised succinct interest points"
    - "matching features without descriptors implicitly matched interest points imips"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "harmonic networks deep translation and rotation equivariance"
    - "quad networks unsupervised learning to rank for interest point detection"

Connected through hyperedge HE-4 (induced by source paper "self improving visual odometry"):
  with members:
    - "lf net learning local features from images"
    - "sips unsupervised succinct interest points"
    - "futuremapping the computational structure of spatial ai systems"
    - "megadepth learning single view depth prediction from internet photos"
    - "matching features without descriptors implicitly matched interest points imips"
    - "superpoint self supervised interest point detection and description"
    - "convolutional neural network architecture for geometric matching"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-5 (induced by source paper "leveraging deep visual descriptors for hierarchical efficient localization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "sips unsupervised succinct interest points"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "superpoint self supervised interest point detection and description"
    - "data efficient decentralized visual slam"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)