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: "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
  Abstract: Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks. However, this achievement is preceded by extreme manual annotation in order to perform either training from scratch or fine-tuning for the target task. In this work, we propose to fine-tune CNN for image retrieval from a large collection of unordered images in a fully automated manner. We employ state-of-the-art retrieval and Structure-from-Motion (SfM) methods to obtain 3D models, which are used to guide the selection of the training data for CNN fine-tuning. We show that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes.

Connected through hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice"):
  with members:
    - "self supervised learning of depth and ego motion with differentiable bundle adjustment"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "understanding the limitations of cnn based absolute camera pose regression"

Connected through hyperedge HE-2 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"

Connected through hyperedge HE-3 (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-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 "learning global and local consistent representations for unsupervised image retrieval via deep graph diffusion networks"):
  with members:
    - "estimating node importance in knowledge graphs using graph neural networks"
    - "neural graph collaborative filtering"
    - "iterative visual reasoning beyond convolutions"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "end to end learning of deep visual representations for image retrieval"
    - "gated graph sequence neural networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"

Connected through hyperedge HE-6 (induced by source paper "solar second order loss and attention for image retrieval"):
  with members:
    - "explicit spatial encoding for deep local descriptors"
    - "learning with average precision training image retrieval with a listwise loss"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "detect to retrieve efficient regional aggregation for image search"
    - "non local neural networks"
    - "attention is all you need"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-7 (induced by source paper "differentiable top k operator with optimal transport"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "reparameterizable subset sampling via continuous relaxations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "fine grained video categorization with redundancy reduction attention"
    - "neural nearest neighbors networks"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "optnet differentiable optimization as a layer in neural networks"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"

Connected through hyperedge HE-8 (induced by source paper "a cnn based patent image retrieval method for design ideation"):
  with members:
    - "technet technology semantic network based on patent data"
    - "yolov3 an incremental improvement"
    - "deep image retrieval learning global representations for image search"
    - "modeling of technological performance trends using design theory"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "very deep convolutional networks for large scale image recognition"

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)