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: "a large scale dataset and benchmark for similar trademark retrieval"
  Abstract: Trademark retrieval (TR) has become an important yet challenging problem due to an ever increasing trend in trademark applications and infringement incidents. There have been many promising attempts for the TR problem, which, however, fell impracticable since they were evaluated with limited and mostly trivial datasets. In this paper, we provide a large-scale dataset with benchmark queries with which different TR approaches can be evaluated systematically. Moreover, we provide a baseline on this benchmark using the widely-used methods applied to TR in the literature. Furthermore, we identify and correct two important issues in TR approaches that were not addressed before: reversal of contrast, and presence of irrelevant text in trademarks severely affect the TR methods. Lastly, we applied deep learning, namely, several popular Convolutional Neural Network models, to the TR problem. To the best of the authors, this is the first attempt to do so.

Connected through hyperedge HE-1 (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"
    - "proximal policy optimization algorithms"
    - "learning rotation invariant convolutional filters for texture classification"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "component based attention for large scale trademark retrieval"):
  with members:
    - "improved person re identification based on saliency and semantic parsing with deep neural network models"
    - "focal loss for dense object detection"
    - "selective deep convolutional features for image retrieval"
    - "scene text eraser"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "good practice in cnn feature transfer"
    - "detecting text in natural image with connectionist text proposal network"

Connected through hyperedge HE-3 (induced by source paper "open set logo detection and retrieval"):
  with members:
    - "deep learning for logo recognition"
    - "the do s and don ts for cnn based face verification"
    - "mask r cnn"
    - "automatic graphic logo detection via fast region based convolutional networks"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

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)