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: "co domain embedding using deep quadruplet networks for unseen traffic sign recognition"
  Abstract: Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data. However,the acquisition of a large supervised dataset is often challenging. This is also true for intelligent transportation applications, i.e., traffic sign recognition. For example, a model trained with data of one country may not be easily generalized to another country without much data. We propose a novel feature embedding scheme for unseen class classification when the representative class template is given. Traffic signs, unlike other objects, have official images. We perform co-domain embedding using a quadruple relationship from real and synthetic domains. Our quadruplet network fully utilizes the explicit pairwise similarity relationships among samples from different domains. We validate our method on three datasets with two experiments involving one-shot classification and feature generalization. The results show that the proposed method outperforms competing approaches on both seen and unseen classes.

Connected through hyperedge HE-1 (induced by source paper "variational prototyping encoder one shot learning with prototypical images"):
  with members:
    - "prototypical networks for few shot learning"
    - "understanding infographics through textual and visual tag prediction"
    - "open set logo detection and retrieval"
    - "a simple neural attentive meta learner"
    - "deep learning logo detection with data expansion by synthesising context"
    - "deep feature consistent variational autoencoder"
    - "image to image translation with conditional adversarial networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-2 (induced by source paper "disco influence maximization meets network embedding and deep learning"):
  with members:
    - "multi round influence maximization"
    - "deepinf social influence prediction with deep learning"
    - "model independent online learning for influence maximization"
    - "learning combinatorial optimization algorithms over graphs"
    - "node2vec scalable feature learning for networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "deepwalk online learning of social representations"

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)