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: "revisiting im2gps in the deep learning era"
  Abstract: Image geolocalization, inferring the geographic location of an image, is a challenging computer vision problem with many potential applications. The recent state-of-the-art approach to this problem is a deep image classification approach in which the world is spatially divided into cells and a deep network is trained to predict the correct cell for a given image. We propose to combine this approach with the original Im2GPS approach in which a query image is matched against a database of geotagged images and the location is inferred from the retrieved set. We estimate the geographic location of a query image by applying kernel density estimation to the locations of its nearest neighbors in the reference database. Interestingly, we find that the best features for our retrieval task are derived from networks trained with classification loss even though we do not use a classification approach at test time. Training with classification loss outperforms several deep feature learning methods (e.g. Siamese networks with contrastive of triplet loss) more typical for retrieval applications. Our simple approach achieves state-of-the-art geolocalization accuracy while also requiring significantly less training data.

Connected through hyperedge HE-1 (induced by source paper "clusterfit improving generalization of visual representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised pre training of image features on non curated data"
    - "large scale weakly supervised pre training for video action recognition"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-2 (induced by source paper "df slam a deep learning enhanced visual slam system based on deep local features"):
  with members:
    - "lf net learning local features from images"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "local descriptors optimized for average precision"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "superpoint self supervised interest point detection and description"
    - "repeatability is not enough learning affine regions via discriminability"
    - "unsupervised learning of depth and ego motion from video"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

Connected through hyperedge HE-3 (induced by source paper "hardness aware deep metric learning"):
  with members:
    - "hard aware point to set deep metric for person re identification"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"

Connected through hyperedge HE-4 (induced by source paper "presence only geographical priors for fine grained image classification"):
  with members:
    - "geo aware networks for fine grained recognition"
    - "class balanced loss based on effective number of samples"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bias reduction via end to end shift learning application to citizen science"
    - "multi entity dependence learning with rich context via conditional variational auto encoder"
    - "pairwise confusion for fine grained visual classification"
    - "diversified visual attention networks for fine grained object classification"
    - "residual attention network for image classification"

Connected through hyperedge HE-5 (induced by source paper "image based geo localization using satellite imagery"):
  with members:
    - "cross view image matching for geo localization in urban environments"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "satellite image based localization via learned embeddings"
    - "deep metric learning with angular loss"
    - "localizing and orienting street views using overhead imagery"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-6 (induced by source paper "protecting geolocation privacy of photo collections"):
  with members:
    - "cross view image matching for geo localization in urban environments"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "planet photo geolocation with convolutional neural networks"
    - "protecting locations with differential privacy under temporal correlations"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "imagenet large scale visual recognition challenge"
    - "intriguing properties of neural networks"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-7 (induced by source paper "hotels 50k a global hotel recognition dataset"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "deep learning features at scale for visual place recognition"
    - "combating human trafficking with deep multimodal models"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "talk the walk navigating new york city through grounded dialogue"):
  with members:
    - "active neural localization"
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "unifying map and landmark based representations for visual navigation"
    - "emergent communication in a multi modal multi step referential game"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "modulating early visual processing by language"
    - "learning visually grounded sentence representations"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

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)