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: "zero shot learning via latent space encoding"
  Abstract: Zero-Shot Learning (ZSL) is typically achieved by resorting to a class semantic embedding space to transfer the knowledge from the seen classes to unseen ones. Capturing the common semantic characteristics between the visual modality and the class semantic modality (e.g., attributes or word vector) is a key to the success of ZSL. In this paper, we present a novel approach called Latent Space Encoding (LSE) for ZSL based on an encoder-decoder framework, which learns a highly effective latent space to well reconstruct both the visual space and the semantic embedding space. For each modality, the encoderdecoder framework jointly maximizes the recoverability of the original space from the latent space and the predictability of the latent space from the original space, thus making the latent space feature-aware. To relate the visual and class semantic modalities together, their features referring to the same concept are enforced to share the same latent codings. In this way, the semantic relations of different modalities are generalized with the latent representations. We also show that the proposed encoder-decoder framework is easily extended to more modalities. Extensive experimental results on four benchmark datasets (AwA, CUB, aPY, and ImageNet) clearly demonstrate the superiority of the proposed approach on several ZSL tasks, including traditional ZSL, generalized ZSL, and zero-shot retrieval (ZSR).

Connected through hyperedge HE-1 (induced by source paper "stacked semantic guided attention model for fine grained zero shot learning"):
  with members:
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "transductive zero shot learning with a self training dictionary approach"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "semantically consistent regularization for zero shot recognition"

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)