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 covariant feature detectors"
  Abstract: Local covariant feature detection, namely the problem of extracting viewpoint invariant features from images, has so far largely resisted the application of machine learning techniques. In this paper, we propose the first fully general formulation for learning local covariant feature detectors. We propose to cast detection as a regression problem, enabling the use of powerful regressors such as deep neural networks. We then derive a covariance constraint that can be used to automatically learn which visual structures provide stable anchors for local feature detection. We support these ideas theoretically, proposing a novel analysis of local features in term of geometric transformations, and we show that all common and many uncommon detectors can be derived in this framework. Finally, we present empirical results on translation and rotation covariant detectors on standard feature benchmarks, showing the power and flexibility of the framework.

Connected through hyperedge HE-1 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "object landmark discovery through unsupervised adaptation"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "learning correspondence from the cycle consistency of time"
    - "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "simple baselines for human pose estimation and tracking"
    - "unsupervised discovery of object landmarks as structural representations"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-3 (induced by source paper "unsupervised part based disentangling of object shape and appearance"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "unsupervised discovery of object landmarks as structural representations"
    - "unsupervised disentangled representation learning with analogical relations"
    - "dgpose disentangled semi supervised deep generative models for human body analysis"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "in defense of the triplet loss for person re identification"
    - "pose guided person image generation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of landmarks by descriptor vector exchange"):
  with members:
    - "learning landmarks from unaligned data using image translation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "densereg fully convolutional dense shape regression in the wild"
    - "self supervised learning of a facial attribute embedding from video"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "unsupervised discovery of object landmarks as structural representations"
    - "voxceleb2 deep speaker recognition"

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"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "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"

Connected through hyperedge HE-6 (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-7 (induced by source paper "gift learning transformation invariant dense visual descriptors via group cnns"):
  with members:
    - "equivariant multi view networks"
    - "equivariant transformer networks"
    - "an improved learning framework for covariant local feature detection"
    - "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"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "graph based isometry invariant representation learning"

Connected through hyperedge HE-8 (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"

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)