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: "deep image retrieval learning global representations for image search"
  Abstract: We propose a novel approach for instance-level image retrieval. It produces a global and compact fixed-length representation for each image by aggregating many region-wise descriptors. In contrast to previous works employing pre-trained deep networks as a black box to produce features, our method leverages a deep architecture trained for the specific task of image retrieval. Our contribution is twofold: (i) we leverage a ranking framework to learn convolution and projection weights that are used to build the region features; and (ii) we employ a region proposal network to learn which regions should be pooled to form the final global descriptor. We show that using clean training data is key to the success of our approach. To that aim, we use a large scale but noisy landmark dataset and develop an automatic cleaning approach. The proposed architecture produces a global image representation in a single forward pass. Our approach significantly outperforms previous approaches based on global descriptors on standard datasets. It even surpasses most prior works based on costly local descriptor indexing and spatial verification. Additional material is available at www.xrce.xerox.com/Deep-Image-Retrieval.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"

Connected through hyperedge HE-2 (induced by source paper "unifying deep local and global features for image search"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "local features and visual words emerge in activations"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "learning with average precision training image retrieval with a listwise loss"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "solar second order loss and attention for image retrieval"):
  with members:
    - "explicit spatial encoding for deep local descriptors"
    - "learning with average precision training image retrieval with a listwise loss"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "detect to retrieve efficient regional aggregation for image search"
    - "non local neural networks"
    - "attention is all you need"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-4 (induced by source paper "differentiable top k operator with optimal transport"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "reparameterizable subset sampling via continuous relaxations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "fine grained video categorization with redundancy reduction attention"
    - "neural nearest neighbors networks"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "optnet differentiable optimization as a layer in neural networks"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"

Connected through hyperedge HE-5 (induced by source paper "augmenting visual place recognition with structural cues"):
  with members:
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "learning 3d segment descriptors for place recognition"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "fine tuning cnn image retrieval with no human annotation"
    - "segmatch segment based place recognition in 3d point clouds"
    - "direct sparse odometry"

Connected through hyperedge HE-6 (induced by source paper "geometric image correspondence verification by dense pixel matching"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neighbourhood consensus networks"
    - "dgc net dense geometric correspondence network"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "image patch matching using convolutional descriptors with euclidean distance"

Connected through hyperedge HE-7 (induced by source paper "end to end supervised product quantization for image search and retrieval"):
  with members:
    - "learning hash codes via hamming distance targets"
    - "deep supervised discrete hashing"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "identity mappings in deep residual networks"
    - "learning to hash with binary deep neural network"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-8 (induced by source paper "neural fuzzy extractors a secure way to use artificial neural networks for biometric user authentication"):
  with members:
    - "fingerprints fixed length representation via deep networks and domain knowledge"
    - "in defense of the triplet loss for person re identification"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "very deep convolutional networks for large scale image recognition"
    - "low density lattice codes"

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)