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: "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
  Abstract: Interest point descriptors have fueled progress on almost every problem in computer vision. Recent advances in deep neural networks have enabled task-specific learned descriptors that outperform hand-crafted descriptors on many problems. We demonstrate that commonly used metric learning approaches do not optimally leverage the feature hierarchies learned in a Convolutional Neural Network (CNN), especially when applied to the task of geometric feature matching. While a metric loss applied to the deepest layer of a CNN, is often expected to yield ideal features irrespective of the task, in fact the growing receptive field as well as striding effects cause shallower features to be better at high precision matching tasks. We leverage this insight together with explicit supervision at multiple levels of the feature hierarchy for better regularization, to learn more effective descriptors in the context of geometric matching tasks. Further, we propose to use activation maps at different layers of a CNN, as an effective and principled replacement for the multi-resolution image pyramids often used for matching tasks. We propose concrete CNN architectures employing these ideas, and evaluate them on multiple datasets for 2D and 3D geometric matching as well as optical flow, demonstrating state-of-the-art results and generalization across datasets.

Connected through hyperedge HE-1 (induced by source paper "r2d2 repeatable and reliable detector and descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "inloc indoor visual localization with dense matching and view synthesis"

Connected through hyperedge HE-2 (induced by source paper "hyperpixel flow semantic correspondence with multi layer neural features"):
  with members:
    - "semantic matching by weakly supervised 2d point set registration"
    - "exploring randomly wired neural networks for image recognition"
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "attentive semantic alignment with offset aware correlation kernels"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "d2 net a trainable cnn for joint detection and description of local features"):
  with members:
    - "lf net learning local features from images"
    - "megadepth learning single view depth prediction from internet photos"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "superpoint self supervised interest point detection and description"
    - "matching neural paths transfer from recognition to correspondence search"
    - "convolutional neural network architecture for geometric matching"
    - "repeatability is not enough learning affine regions via discriminability"
    - "semantic visual localization"

Connected through hyperedge HE-4 (induced by source paper "gcnv2 efficient correspondence prediction for real time slam"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "single view stereo matching"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "superpoint self supervised interest point detection and description"
    - "convolutional neural network architecture for geometric matching"
    - "improving landmark localization with semi supervised learning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-5 (induced by source paper "scale adaptive neural dense features learning via hierarchical context aggregation"):
  with members:
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "dispsegnet leveraging semantics for end to end learning of disparity estimation from stereo imagery"
    - "pairwise relational networks for face recognition"
    - "the contextual loss for image transformation with non aligned data"
    - "deep metric learning with hierarchical triplet loss"
    - "pyramid stereo matching network"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "degeneracy in self calibration revisited and a deep learning solution for uncalibrated slam"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "baseline desensitizing in translation averaging"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "rolling shutter aware differential sfm and image rectification"
    - "deep supervision with intermediate concepts"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-7 (induced by source paper "from coarse to fine robust hierarchical localization at large scale"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "vizard reliable visual localization for autonomous vehicles in urban outdoor environments"
    - "lf net learning local features from images"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"

Connected through hyperedge HE-8 (induced by source paper "dgc net dense geometric correspondence network"):
  with members:
    - "semi supervised semantic matching"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "semantic visual localization"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "image patch matching using convolutional descriptors with euclidean distance"

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)