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: "fine tuning cnn image retrieval with no human annotation"
  Abstract: Image descriptors based on activations of Convolutional Neural Networks (CNNs) have become dominant in image retrieval due to their discriminative power, compactness of the representation, and the efficiency of search. Training of CNNs, either from scratch or fine-tuning, requires a large amount of annotated data, where high quality of the annotation is often crucial. In this work, we propose to fine-tune CNNs for image retrieval on a large collection of unordered images in a fully automatic manner. Reconstructed 3D models, obtained by the state-of-the-art retrieval and structure-from-motion methods, guide the selection of the training data. We show that both hard positive and hard negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance in particular object retrieval. CNN descriptor whitening discriminatively learned from the same training data outperforms the commonly used PCA whitening. We propose a novel trainable Generalized-Mean (GeM) pooling layer that generalizes max and average pooling and show that it boosts retrieval performance. Applying the proposed method on VGG network achieves state-of-the-art performance on standard benchmarks: Oxford Buildings, Paris, and Holidays datasets.

Connected through hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation"):
  with members:
    - "badgr an autonomous self supervised learning based navigation system"
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "beyond photometric loss for self supervised ego motion estimation"
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "self supervised learning of depth and motion under photometric inconsistency"

Connected through hyperedge HE-2 (induced by source paper "os2d one stage one shot object detection by matching anchor features"):
  with members:
    - "ranked list loss for deep metric learning"
    - "one shot object detection with co attention and co excitation"
    - "one shot instance segmentation"
    - "neighbourhood consensus networks"
    - "repmet representative based metric learning for classification and one shot object detection"
    - "lstd a low shot transfer detector for object detection"
    - "end to end representation learning for correlation filter based tracking"
    - "end to end weakly supervised semantic alignment"

Connected through hyperedge HE-3 (induced by source paper "unifying deep local and global features for image search"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "local features and visual words emerge in activations"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "learning with average precision training image retrieval with a listwise loss"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-4 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection"):
  with members:
    - "towards partial supervision for generic object counting in natural scenes"
    - "fine grained recognition accounting for subtle differences between similar classes"
    - "3c net category count and center loss for weakly supervised action localization"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "object counting and instance segmentation with image level supervision"
    - "occlusion aware r cnn detecting pedestrians in a crowd"

Connected through hyperedge HE-5 (induced by source paper "an adaptive random path selection approach for incremental learning"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual lifelong learning with neural networks a review"
    - "exploring randomly wired neural networks for image recognition"
    - "random path selection for incremental learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep generative dual memory network for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "generative replay with feedback connections as a general strategy for continual learning"

Connected through hyperedge HE-6 (induced by source paper "university 1652 a multi view multi source benchmark for drone based geo localization"):
  with members:
    - "building information modeling and classification by visual learning at a city scale"
    - "triplet based deep hashing network for cross modal retrieval"
    - "lending orientation to neural networks for cross view geo localization"
    - "optimal feature transport for cross view image geo localization"
    - "vision meets drones a challenge"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-7 (induced by source paper "progressive local filter pruning for image retrieval acceleration"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "learning sparse sharing architectures for multiple tasks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "paraphrasing complex network network compression via factor transfer"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "channel pruning for accelerating very deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-8 (induced by source paper "augmenting visual place recognition with structural cues"):
  with members:
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "learning 3d segment descriptors for place recognition"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "segmatch segment based place recognition in 3d point clouds"
    - "deep image retrieval learning global representations for image search"
    - "direct sparse odometry"

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)