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: "attribute based synthetic network abs net learning more from pseudo feature representations"
  Abstract: Abstract   In large-scale visual recognition tasks, researchers are usually faced with some challenging problems, such as the extreme imbalance in the number of training data between classes or the lack of annotated data for some classes. In this paper, we propose a novel neural network architecture that automatically synthesizes pseudo feature representations for the classes in lack of annotated images. With the supply of semantic attributes for classes, the proposed Attribute-Based Synthetic Network (ABS-Net) can be applied to zero-shot learning (ZSL) scenario and conventional supervised learning (CSL) scenario as well. For ZSL tasks, the pseudo feature representations can be viewed as annotated feature-level instances for novel concepts, which facilitates the construction of unseen class predictor. For CSL tasks, the pseudo feature representations can be viewed as products of data augmentation on training set, which enriches the interpretation capacity of CSL systems. We demonstrate the effectiveness of the proposed ABS-Net in ZSL and CSL settings on a synthetic colored MNIST dataset (C-MNIST). For several popular ZSL benchmark datasets, our architecture also shows competitive results on zero-shot recognition task, especially leading to tremendous improvement to state-of-the-art mAP on zero-shot retrieval task.

Connected through hyperedge HE-1 (induced by source paper "a generative framework for zero shot learning with adversarial domain adaptation"):
  with members:
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "transductive unbiased embedding for zero shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "cycada cycle consistent adversarial domain adaptation"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "zero shot image classification using coupled dictionary embedding"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "joint dictionaries 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-3 (induced by source paper "stacked semantic guided attention model for fine grained zero shot learning"):
  with members:
    - "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"
    - "zero shot learning via latent space encoding"

Connected through hyperedge HE-4 (induced by source paper "attributes as operators"):
  with members:
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "joint discovery of object states and manipulation actions"
    - "alternative semantic representations for zero shot human action recognition"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "visual translation embedding network for visual relation detection"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"

Connected through hyperedge HE-5 (induced by source paper "zero shot learning via latent space encoding"):
  with members:
    - "transductive zero shot learning with a self training dictionary approach"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "transductive zero shot learning with adaptive structural embedding"
    - "learning a deep embedding model for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "semi supervised vocabulary informed learning"
    - "transductive multi view zero shot 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)