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: "a comparison of dense region detectors for image search and fine grained classification"
  Abstract: We consider a pipeline for image classification or search based on coding approaches like bag of words or Fisher vectors. In this context, the most common approach is to extract the image patches regularly in a dense manner on several scales. This paper proposes and evaluates alternative choices to extract patches densely. Beyond simple strategies derived from regular interest region detectors, we propose approaches based on superpixels, edges, and a bank of Zernike filters used as detectors. The different approaches are evaluated on recent image retrieval and fine-grained classification benchmarks. Our results show that the regular dense detector is outperformed by other methods in most situations, leading us to improve the state-of-the-art in comparable setups on standard retrieval and fined-grained benchmarks. As a byproduct of our study, we show that existing methods for blob and superpixel extraction achieve high accuracy if the patches are extracted along the edges and not around the detected regions.

Connected through hyperedge HE-1 (induced by source paper "object part attention model for fine grained image classification"):
  with members:
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "interactive inter layer activeness propagation"
    - "mining discriminative triplets of patches for fine grained classification"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "fine grained image classification by exploring bipartite graph labels"

Connected through hyperedge HE-2 (induced by source paper "dynamicity and durability in scalable visual instance search"):
  with members:
    - "faster r cnn features for instance search"
    - "siamese instance search for tracking"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-3 (induced by source paper "fine grained categorization via cnn based automatic extraction and integration of object level and part level features"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "mining discriminative triplets of patches for fine grained classification"
    - "deep learning in neural networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "part stacked cnn for fine grained visual categorization"

Connected through hyperedge HE-4 (induced by source paper "sift meets cnn a decade survey of instance retrieval"):
  with members:
    - "polysemous codes"
    - "faster r cnn features for instance search"
    - "lift learned invariant feature transform"
    - "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"
    - "good practice in cnn feature transfer"
    - "a survey on learning to hash"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised fine grained categorization with part based image representation"):
  with members:
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "fast r cnn"
    - "r cnn minus r"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fine grained recognition datasets for biodiversity analysis"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-6 (induced by source paper "a practical guide to cnns and fisher vectors for image instance retrieval"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "deep image scaling up image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "a discriminative cnn video representation for event detection"

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)