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: "saliency based sequential image attention with multiset prediction"
  Abstract: Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.

Connected through hyperedge HE-1 (induced by source paper "sequential graph dependency parser"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "unsupervised recurrent neural network grammars"
    - "universal dependency parsing from scratch"
    - "left to right dependency parsing with pointer networks"
    - "non monotonic sequential text generation"
    - "learning deep generative models of graphs"
    - "the importance of generation order in language modeling"

Connected through hyperedge HE-2 (induced by source paper "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"):
  with members:
    - "deep sets"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "deep supervised discrete hashing"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "structured generative adversarial networks"
    - "first order adaptive sample size methods to reduce complexity of empirical risk minimization"
    - "consistent multitask learning with nonlinear output relations"

Connected through hyperedge HE-3 (induced by source paper "loss functions for multiset prediction"):
  with members:
    - "mask r cnn"
    - "deepsetnet predicting sets with deep neural networks"
    - "end to end instance segmentation with recurrent attention"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "learning to search better than your teacher"
    - "recurrent instance segmentation"

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)