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: "aggregating deep convolutional features for image retrieval"
  Abstract: Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It has also been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregation approaches developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptors. #R##N#In this paper we investigate possible ways to aggregate local deep features to produce compact global descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides arguably the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.

Connected through hyperedge HE-1 (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-2 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (induced by source paper "targeted mismatch adversarial attack query with a flower to retrieve the tower"):
  with members:
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "universal perturbation attack against image retrieval"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "open set adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-4 (induced by source paper "semi supervised feature level attribute manipulation for fashion image retrieval"):
  with members:
    - "combination of multiple global descriptors for image retrieval"
    - "learning type aware embeddings for fashion compatibility"
    - "hard aware point to set deep metric for person re identification"
    - "give me a hint navigating image databases using human in the loop feedback"
    - "began boundary equilibrium generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "squeeze and excitation networks"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"

Connected through hyperedge HE-5 (induced by source paper "variational metric scaling for metric based meta learning"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "on first order meta learning algorithms"
    - "rethinking feature distribution for loss functions in image classification"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "few shot object detection via feature reweighting"
    - "gaussian prototypical networks for few shot learning on omniglot"
    - "probabilistic model agnostic meta learning"
    - "large margin few shot learning"

Connected through hyperedge HE-6 (induced by source paper "local features and visual words emerge in activations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "neighbourhood consensus networks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "re id done right towards good practices for person re identification"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "lift learned invariant feature transform"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"

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 "deep aggregation of regional convolutional activations for content based image retrieval"):
  with members:
    - "an accurate retrieval through r mac descriptors for landmark recognition"
    - "fine tuning cnn image retrieval with no human annotation"
    - "what are the receptive effective receptive and projective fields of neurons in convolutional neural networks"
    - "end to end learning of deep visual representations for image retrieval"
    - "identity mappings in deep residual networks"
    - "deep metric learning via facility location"
    - "large scale image retrieval with attentive deep local features"
    - "deep residual learning for 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)