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: "training a convolutional neural network for appearance invariant place recognition"
  Abstract: Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop closure in visual SLAM systems. Moreover, the difficulty of recognizing a revisited location increases with appearance changes caused, for instance, by weather or illumination variations, which hinders the long-term application of such algorithms in real environments. In this paper we present a convolutional neural network (CNN), trained for the first time with the purpose of recognizing revisited locations under severe appearance changes, which maps images to a low dimensional space where Euclidean distances represent place dissimilarity. In order for the network to learn the desired invariances, we train it with triplets of images selected from datasets which present a challenging variability in visual appearance. The triplets are selected in such way that two samples are from the same location and the third one is taken from a different place. We validate our system through extensive experimentation, where we demonstrate better performance than state-of-art algorithms in a number of popular datasets.

Connected through hyperedge HE-1 (induced by source paper "condition invariant multi view place recognition"):
  with members:
    - "loosely coupled semi direct monocular slam"
    - "single view place recognition under seasonal changes"
    - "deep learning features at scale for visual place recognition"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "deep residual learning for image recognition"
    - "on the performance of convnet features for place recognition"

Connected through hyperedge HE-2 (induced by source paper "semantically aware attentive neural embeddings for image based visual localization"):
  with members:
    - "deep audio visual speech recognition"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "attention based pyramid aggregation network for visual place recognition"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "night to day image translation for retrieval based localization"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "long term visual localization using semantically segmented images"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-3 (induced by source paper "excavate condition invariant space by intrinsic encoder"):
  with members:
    - "binary generative adversarial networks for image retrieval"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "deep learning features at scale for visual place recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "addressing challenging place recognition tasks using generative adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "single view place recognition under seasonal changes"):
  with members:
    - "deep learning features at scale for visual place recognition"
    - "tilde a temporally invariant learned detector"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "orb slam a versatile and accurate monocular slam system"
    - "on the performance of convnet features for place recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "toward geometric deep slam"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "demon depth and motion network for learning monocular stereo"
    - "lift learned invariant feature transform"
    - "deep image homography estimation"
    - "unsupervised monocular depth estimation with left right consistency"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "dsac differentiable ransac for camera localization"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-6 (induced by source paper "deep learning features at scale for visual place recognition"):
  with members:
    - "visual place recognition using landmark distribution descriptors"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "convolutional neural network based place recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-7 (induced by source paper "learning metrics by learning constrained embeddings of objects to rn"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "cudnn efficient primitives for deep learning"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "fast metric learning for deep neural networks"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "cudnn efficient primitives for deep learning"
    - "fast supervised hashing with decision trees for high dimensional data"

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)