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: "scalable deep learning logo detection"
  Abstract: Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL^2), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset "WebLogo-2M" by an automatic web data collection and processing method. Extensive comparative evaluations demonstrate the superiority of the proposed SL^2 method over the state-of-the-art strongly and weakly supervised detection models and contemporary webly data learning approaches.

Connected through hyperedge HE-1 (induced by source paper "a deep one shot network for query based logo retrieval"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "texture synthesis guided deep hashing for texture image retrieval"
    - "multi scale location aware kernel representation for object detection"
    - "open logo detection challenge"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "focal loss for dense object detection"
    - "deep learning for logo recognition"

Connected through hyperedge HE-2 (induced by source paper "open logo detection challenge"):
  with members:
    - "deep learning for logo recognition"
    - "open set logo detection and retrieval"
    - "synthesizing training data for object detection in indoor scenes"
    - "yolo9000 better faster stronger"
    - "generative image modeling using style and structure adversarial networks"
    - "deep learning logo detection with data expansion by synthesising context"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)