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: "visual instance retrieval with deep convolutional networks"
  Abstract: This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately.

Connected through hyperedge HE-1 (induced by source paper "deeply activated salient region for instance search"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "weakly supervised instance segmentation using class peak response"
    - "yolov3 an incremental improvement"
    - "instance search via instance level segmentation and feature representation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "saliency weighted convolutional features for instance search"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "continuous control with contexts provably"
    - "learning agile and dynamic motor skills for legged robots"
    - "h_ infty model free reinforcement learning with robust stability guarantee"
    - "robust model free reinforcement learning with multi objective bayesian optimization"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "an analysis of object embeddings for image retrieval"):
  with members:
    - "learning rich features for image manipulation detection"
    - "do better imagenet models transfer better"
    - "deep learning for generic object detection a survey"
    - "dgc net dense geometric correspondence network"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "the ps battles dataset an image collection for image manipulation detection"
    - "sniper efficient multi scale training"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-5 (induced by source paper "unsupervised semantic based aggregation of deep convolutional features"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "residual attention network for image classification"
    - "mask r cnn"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-6 (induced by source paper "metalgan a cluster based adaptive training for few shot adversarial colorization"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "on first order meta learning algorithms"
    - "learning to optimize neural nets"
    - "a zero shot framework for sketch based image retrieval"
    - "texturegan controlling deep image synthesis with texture patches"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learned optimizers that scale and generalize"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "sparse to dense hypercolumn matching for long term visual localization"):
  with members:
    - "lf net learning local features from images"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "improving nighttime retrieval based localization"
    - "scene coordinate and correspondence learning for image based localization"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "night to day image translation for retrieval based localization"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "inloc indoor visual localization with dense matching and view synthesis"

Connected through hyperedge HE-8 (induced by source paper "explicit shape encoding for real time instance segmentation"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bag of tricks for image classification with convolutional neural networks"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "yolov3 an incremental improvement"
    - "deep rnn framework for visual sequential applications"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "semi convolutional operators for instance segmentation"

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)