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: "saliency weighted convolutional features for instance search"
  Abstract: This work explores attention models to weight the contribution of local convolutional representations for the instance search task. We present a retrieval framework based on bags of local convolutional features (BLCF) that benefits from saliency weighting to build an efficient image representation. The use of human visual attention models (saliency) allows significant improvements in retrieval performance without the need to conduct region analysis or spatial verification, and without requiring any feature fine tuning. We investigate the impact of different saliency models, finding that higher performance on saliency benchmarks does not necessarily equate to improved performance when used in instance search tasks. The proposed approach outperforms the state-of-the-art on the challenging INSTRE benchmark by a large margin, and provides similar performance on the Oxford and Paris benchmarks compared to more complex methods that use off-the-shelf representations. The source code used in this project is available at this https URL

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"
    - "multiple instance detection network with online instance classifier refinement"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "benchmarking unsupervised near duplicate image detection"):
  with members:
    - "an anti fraud system for car insurance claim based on visual evidence"
    - "billion scale similarity search with gpus"
    - "recent advance in content based image retrieval a literature survey"
    - "end to end learning of deep visual representations for image retrieval"
    - "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"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "on guiding video object segmentation"):
  with members:
    - "focal loss for dense object detection"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning video object segmentation with visual memory"
    - "semantic segmentation with reverse attention"
    - "video propagation networks"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "attention based pyramid aggregation network for visual place recognition"):
  with members:
    - "class weighted convolutional features for visual instance search"
    - "selective deep convolutional features for image retrieval"
    - "squeeze and excitation networks"
    - "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:
    - "class weighted convolutional features for visual instance search"
    - "a location aware embedding technique for accurate landmark recognition"
    - "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)