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: "selective deep convolutional features 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, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectively address the burstiness issue and improve retrieval accuracy. Secondly, we propose to employ recent embedding and aggregating methods to further enhance feature discriminability. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art retrieval accuracy.

Connected through hyperedge HE-1 (induced by source paper "combination of multiple global descriptors for image retrieval"):
  with members:
    - "large scale landmark retrieval recognition under a noisy and diverse dataset"
    - "2nd place and 2nd place solution to kaggle landmark recognition andretrieval competition 2019"
    - "visualizing deep similarity networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "learning discriminative features with multiple granularities for person re identification"
    - "classification is a strong baseline for deep metric learning"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "fda feature disruptive attack"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "adversarial logit pairing"
    - "gray box adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "on device scalable image based localization via prioritized cascade search and fast one many ransac"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "image based localization using lstms for structured feature correlation"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "learning to hash with binary deep neural network"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "metric localization using google street view"

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"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "smart mining for deep metric learning"
    - "faster r cnn features for instance search"
    - "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 "attention based pyramid aggregation network for visual place recognition"):
  with members:
    - "class weighted convolutional features for visual instance search"
    - "squeeze and excitation networks"
    - "saliency weighted convolutional features for instance search"
    - "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-6 (induced by source paper "from selective deep convolutional features to compact binary representations for image retrieval"):
  with members:
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "exploiting depth from single monocular images for object detection and semantic segmentation"
    - "fine tuning cnn image retrieval with no human annotation"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "deep image retrieval learning global representations for image search"
    - "embedding based on function approximation for large scale image search"
    - "learning to hash with binary deep neural network"

Connected through hyperedge HE-7 (induced by source paper "deep feature aggregation and image re ranking with heat diffusion for image retrieval"):
  with members:
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "fast spectral ranking for similarity search"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "voronoi based compact image descriptors efficient region of interest retrieval with vlad and deep learning based descriptors"
    - "deep image retrieval learning global representations for image search"
    - "embedding based on function approximation for large scale image search"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"

Connected through hyperedge HE-8 (induced by source paper "efficient and deep person re identification using multi level similarity"):
  with members:
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "learning to hash with binary deep neural network"
    - "a multi task deep network for person re identification"
    - "learning to rank in person re identification with metric ensembles"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)