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: "emergence of foveal image sampling from learning to attend in visual scenes"
  Abstract: We describe a neural attention model with a learnable retinal sampling lattice. The model is trained on a visual search task requiring the classification of an object embedded in a visual scene amidst background distractors using the smallest number of fixations. We explore the tiling properties that emerge in the model's retinal sampling lattice after training. Specifically, we show that this lattice resembles the eccentricity dependent sampling lattice of the primate retina, with a high resolution region in the fovea surrounded by a low resolution periphery. Furthermore, we find conditions where these emergent properties are amplified or eliminated providing clues to their function.

Connected through hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"

Connected through hyperedge HE-2 (induced by source paper "contextual encoder decoder network for visual saliency prediction"):
  with members:
    - "influence of image classification accuracy on saliency map estimation"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "invariance analysis of saliency models versus human gaze during scene free viewing"
    - "eml net an expandable multi layer network for saliency prediction"
    - "end to end saliency mapping via probability distribution prediction"
    - "saliency benchmarking made easy separating models maps and metrics"
    - "dual path networks"

Connected through hyperedge HE-3 (induced by source paper "feratt facial expression recognition with attention net"):
  with members:
    - "fast and robust multiple colorchecker detection using deep convolutional neural networks"
    - "learning dual convolutional neural networks for low level vision"
    - "deep facial expression recognition a survey"
    - "enhanced deep residual networks for single image super resolution"
    - "zero shot super resolution using deep internal learning"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "assessment of faster r cnn in man machine collaborative search"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "attention allocation aid for visual search"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "improving small object proposals for company logo detection"
    - "machine teaching a new paradigm for building machine learning systems"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-5 (induced by source paper "saliency based sequential image attention with multiset prediction"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "feudal networks for hierarchical reinforcement learning"
    - "paying more attention to saliency image captioning with saliency and context attention"
    - "towards instance segmentation with object priority prominent object detection and recognition"
    - "hierarchical attention networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "generating visual explanations"

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.NE(Neural and Evolutionary Computing)