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: "joint dictionaries for zero shot learning"
  Abstract: A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes of data (e.g. visual data). In this paper, we propose to learn a visual feature dictionary that has semantically meaningful atoms. Such dictionary is learned via joint dictionary learning for the visual domain and the attribute domain, while enforcing the same sparse coding for both dictionaries. Our novel attribute aware formulation provides an algorithmic solution to the domain shift/hubness problem in ZSL. Upon learning the joint dictionaries, images from unseen classes can be mapped into the attribute space by finding the attribute aware joint sparse representation using solely the visual data. We demonstrate that our approach provides superior or comparable performance to that of the state of the art on benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "joint concept matching based learning for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "structured analysis dictionary learning for image classification"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "feature generating networks for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "joint concept matching space projection learning for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-3 (induced by source paper "zero shot image classification using coupled dictionary embedding"):
  with members:
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "semantic autoencoder for zero shot learning"
    - "using task descriptions in lifelong machine learning for improved performance and zero shot transfer"
    - "learning a deep embedding model for zero shot learning"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-4 (induced by source paper "grounding language for transfer in deep reinforcement learning"):
  with members:
    - "cross domain transfer in reinforcement learning using target apprentice"
    - "progressive neural networks"
    - "attention is all you need"
    - "guiding reinforcement learning exploration using natural language"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"

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)