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: "exploiting local features from deep networks for image retrieval"
  Abstract: Deep convolutional neural networks have been successfully applied to image classification tasks. When these same networks have been applied to image retrieval, the assumption has been made that the last layers would give the best performance, as they do in classification. We show that for instance-level image retrieval, lower layers often perform better than the last layers in convolutional neural networks. We present an approach for extracting convolutional features from different layers of the networks, and adopt VLAD encoding to encode features into a single vector for each image. We investigate the effect of different layers and scales of input images on the performance of convolutional features using the recent deep networks OxfordNet and GoogLeNet. Experiments demonstrate that intermediate layers or higher layers with finer scales produce better results for image retrieval, compared to the last layer. When using compressed 128-D VLAD descriptors, our method obtains state-of-the-art results and outperforms other VLAD and CNN based approaches on two out of three test datasets. Our work provides guidance for transferring deep networks trained on image classification to image retrieval tasks.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "boosting few shot visual learning with self supervision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "momentum contrast for unsupervised visual representation learning"

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 "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-4 (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-5 (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"

Connected through hyperedge HE-6 (induced by source paper "generate non software bugs to fool classifiers"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "robust audio adversarial example for a physical attack"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial audio synthesis"
    - "generating adversarial examples with adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "approximating cnns with bag of local features models works surprisingly well on imagenet"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "scale coding bag of deep features for human attribute and action recognition"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "hcp a flexible cnn framework for multi label image classification"
    - "bags of local convolutional features for scalable instance search"

Connected through hyperedge HE-8 (induced by source paper "scaling video analytics on constrained edge nodes"):
  with members:
    - "precision and recall for range based anomaly detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "fully convolutional siamese networks for object tracking"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "end to end learning of action detection from frame glimpses in videos"
    - "action recognition using visual attention"

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)