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: "transductive zero shot learning with visual structure constraint"
  Abstract: Zero-shot Learning (ZSL) aims to recognize objects of the unseen classes, whose instances may not have been seen during training. It associates seen and unseen classes with the common semantic space and provides the visual features for each data instance. Most existing methods first learn a compatible projection function between the semantic space and the visual space based on the data of source seen classes, then directly apply it to target unseen classes. However, in real scenarios, the data distribution between the source and target domain might not match well, thus causing the well-known domain shift problem. Based on the observation that visual features of test instances can be separated into different clusters, we propose a visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i.e. alleviate the above domain shift problem). Specifically, two different strategies (symmetric Chamfer-distance and bipartite matching) are adopted to align the projected unseen semantic centers and visual cluster centers of test instances. Experiments on three widely used datasets demonstrate that the proposed visual structure constraint can bring substantial performance gain consistently and achieve state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "semantic guided multi attention localization for zero shot learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning feature to feature translator by alternating back propagation for generative zero shot learning"
    - "moment matching for multi source domain adaptation"
    - "discriminative learning of latent features for zero shot recognition"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "normface l 2 hypersphere embedding for face verification"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "transfer feature generating networks with semantic classes structure for zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "discriminative learning of latent features for zero shot recognition"
    - "class label autoencoder for zero shot learning"
    - "structure fusion and propagation for zero shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "multi label zero shot learning with structured knowledge graphs"

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)