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: "class weighted convolutional features for visual instance search"
  Abstract: Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. The source code and model used in this paper are publicly available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "fashion outfit generation for e commerce"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "fashionnet personalized outfit recommendation with deep neural network"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "compatibility family learning for item recommendation and generation"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-2 (induced by source paper "actnet end to end learning of feature activations and aggregation for effective instance image retrieval"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "attention aware generalized mean pooling for image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "aggregated residual transformations for deep neural networks"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-3 (induced by source paper "learning non metric visual similarity for image retrieval"):
  with members:
    - "few example object detection with model communication"
    - "learning deep similarity models with focus ranking for fabric image retrieval"
    - "a simple neural network module for relational reasoning"
    - "no fuss distance metric learning using proxies"
    - "learning to compare relation network for few shot learning"
    - "faster r cnn features for instance search"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"

Connected through hyperedge HE-4 (induced by source paper "attention based pyramid aggregation network for visual place recognition"):
  with members:
    - "selective deep convolutional features for image retrieval"
    - "squeeze and excitation networks"
    - "saliency weighted convolutional features for instance search"
    - "residual attention network for image classification"
    - "neural aggregation network for video face recognition"
    - "deep image retrieval learning global representations for image search"
    - "large scale image retrieval with attentive deep local features"
    - "pyramid scene parsing network"

Connected through hyperedge HE-5 (induced by source paper "an accurate retrieval through r mac descriptors for landmark recognition"):
  with members:
    - "a location aware embedding technique for accurate landmark recognition"
    - "saliency weighted convolutional features for instance search"
    - "context aware query image representation for particular object retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "sift meets cnn a decade survey of instance retrieval"
    - "cross dimensional weighting for aggregated deep convolutional features"

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)