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: "faster r cnn features for instance search"
  Abstract: Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN. We take advantage of the object proposals learned by a Region Proposal Network (RPN) and their associated CNN features to build an instance search pipeline composed of a first filtering stage followed by a spatial reranking. We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve. We assess the performance of our proposed system with the Oxford Buildings 5k, Paris Buildings 6k and a subset of TRECVid Instance Search 2013, achieving competitive results.

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 "a fast and accurate one stage approach to visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "neural sequential phrase grounding seqground"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "yolov3 an incremental improvement"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "unsupervised image captioning"
    - "mattnet modular attention network for referring expression comprehension"

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"
    - "class weighted convolutional features for visual instance search"
    - "a simple neural network module for relational reasoning"
    - "no fuss distance metric learning using proxies"
    - "learning to compare relation network for few shot learning"
    - "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 "binary constrained deep hashing network for image retrieval without human intervention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "selective deep convolutional features for image retrieval"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "smart mining for deep metric learning"
    - "fast training of triplet based deep binary embedding networks"
    - "embedding based on function approximation for large scale image search"
    - "a survey on learning to hash"

Connected through hyperedge HE-5 (induced by source paper "tattoo image search at scale joint detection and compact representation learning"):
  with members:
    - "fast supervised discrete hashing"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "subic a supervised structured binary code for image search"
    - "training region based object detectors with online hard example mining"
    - "sift meets cnn a decade survey of instance retrieval"
    - "joint detection and identification feature learning for person search"
    - "learning to hash for indexing big data a survey"

Connected through hyperedge HE-6 (induced by source paper "instance search via instance level segmentation and feature representation"):
  with members:
    - "deformable convolutional networks"
    - "aggregated residual transformations for deep neural networks"
    - "good practice in cnn feature transfer"
    - "fully convolutional instance aware semantic segmentation"
    - "interactive inter layer activeness propagation"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "dynamicity and durability in scalable visual instance search"):
  with members:
    - "siamese instance search for tracking"
    - "a comparison of dense region detectors for image search and fine grained classification"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-8 (induced by source paper "one shot fine grained instance retrieval"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "subset feature learning for fine grained category classification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"

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)