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: "revisiting kernelized locality sensitive hashing for improved large scale image retrieval"
  Abstract: We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel Hilbert space (RKHS). Our new perspective is based on viewing the steps of the KLSH algorithm in an appropriately projected space, and has several key theoretical and practical benefits. First, it eliminates the problematic conceptual difficulties that are present in the existing motivation of KLSH. Second, it yields the first formal retrieval performance bounds for KLSH. Third, our analysis reveals two techniques for boosting the empirical performance of KLSH. We evaluate these extensions on several large-scale benchmark image retrieval data sets, and show that our analysis leads to improved recall performance of at least 12%, and sometimes much higher, over the standard KLSH method.

Connected through hyperedge HE-1 (induced by source paper "sadih semantic aware discrete hashing"):
  with members:
    - "fast supervised discrete hashing"
    - "supervised discrete hashing with relaxation"
    - "highly economized multi view binary compression for scalable image clustering"
    - "a survey on learning to hash"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "very deep convolutional networks for large scale image recognition"
    - "fast supervised hashing with decision trees for high dimensional data"

Connected through hyperedge HE-2 (induced by source paper "supervised quantization for similarity search"):
  with members:
    - "hashing on nonlinear manifolds"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "hashing with binary autoencoders"
    - "optimized cartesian k means"
    - "hashing for similarity search a survey"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "fast neighborhood graph search using cartesian concatenation"

Connected through hyperedge HE-3 (induced by source paper "leveraging implicit spatial information in global features for image retrieval"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-4 (induced by source paper "cvpaper challenge in 2015 a review of cvpr2015 and deepsurvey"):
  with members:
    - "semi supervised learning with explicit relationship regularization"
    - "understanding image virality"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "bilinear random projections for locality sensitive binary codes"
    - "learning multiple visual tasks while discovering their structure"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "fast training of triplet based deep binary embedding networks"):
  with members:
    - "deepid3 face recognition with very deep neural networks"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "face search at scale 80 million gallery"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"
    - "facenet a unified embedding for face recognition and clustering"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "3d binary signatures"):
  with members:
    - "pcanet a simple deep learning baseline for image classification"
    - "imagenet large scale visual recognition challenge"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "rotational projection statistics for 3d local surface description and object recognition"

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)