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: "cross dimensional weighting for aggregated deep convolutional features"
  Abstract: We propose a simple and straightforward way of creating powerful image representations via cross-dimensional weighting and aggregation of deep convolutional neural network layer outputs. We first present a generalized framework that encompasses a broad family of approaches and includes cross-dimensional pooling and weighting steps. We then propose specific non-parametric schemes for both spatial- and channel-wise weighting that boost the effect of highly active spatial responses and at the same time regulate burstiness effects. We experiment on different public datasets for image search and show that our approach outperforms the current state-of-the-art for approaches based on pre-trained networks. We also provide an easy-to-use, open source implementation that reproduces our results.

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 "deeply activated salient region for instance search"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "weakly supervised instance segmentation using class peak response"
    - "yolov3 an incremental improvement"
    - "instance search via instance level segmentation and feature representation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "saliency weighted convolutional features for instance search"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-3 (induced by source paper "enhancing feature invariance with learned image transformations for image retrieval"):
  with members:
    - "component based attention for large scale trademark retrieval"
    - "learning transferable architectures for scalable image recognition"
    - "deformable convolutional networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "a large scale dataset and benchmark for similar trademark retrieval"
    - "proximal policy optimization algorithms"
    - "learning rotation invariant convolutional filters for texture classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "a cnn based patent image retrieval method for design ideation"):
  with members:
    - "technet technology semantic network based on patent data"
    - "yolov3 an incremental improvement"
    - "deep image retrieval learning global representations for image search"
    - "modeling of technological performance trends using design theory"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "unsupervised semantic based aggregation of deep convolutional features"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "residual attention network for image classification"
    - "mask r cnn"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-6 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "mihash online hashing with mutual information"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "vse improving visual semantic embeddings with hard negatives"
    - "fine tuning cnn image retrieval with no human annotation"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-7 (induced by source paper "sparse to dense hypercolumn matching for long term visual localization"):
  with members:
    - "lf net learning local features from images"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "improving nighttime retrieval based localization"
    - "scene coordinate and correspondence learning for image based localization"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "night to day image translation for retrieval based localization"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "inloc indoor visual localization with dense matching and view synthesis"

Connected through hyperedge HE-8 (induced by source paper "targeted mismatch adversarial attack query with a flower to retrieve the tower"):
  with members:
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "universal perturbation attack against image retrieval"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "open set adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "practical black box attacks against machine learning"

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)