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: "from selective deep convolutional features to compact binary representations for image retrieval"
  Abstract: Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUMmask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features from a feature map. We demonstrate that proposed masking schemes are effectively to address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Additionally, we include a hashing module to produce compact binary image representations which are effective for the retrieval. Extensive experiments on six image retrieval benchmarks demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.

Connected through hyperedge HE-1 (induced by source paper "btel a binary tree encoding approach for visual localization"):
  with members:
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-2 (induced by source paper "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"):
  with members:
    - "detect to retrieve efficient regional aggregation for image search"
    - "deep learning features at scale for visual place recognition"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "sift meets cnn a decade survey of instance retrieval"
    - "visual place recognition using landmark distribution descriptors"
    - "cross convolutional layer pooling for image recognition"
    - "spatial transformer networks"
    - "particular object retrieval with integral max pooling of cnn activations"

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)