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: "how deep is the feature analysis underlying rapid visual categorization"
  Abstract: Rapid categorization paradigms have a long history in experimental psychology: Characterized by short presentation times and speedy behavioral responses, these tasks highlight the efficiency with which our visual system processes natural object categories. Previous studies have shown that feed-forward hierarchical models of the visual cortex provide a good fit to human visual decisions. At the same time, recent work in computer vision has demonstrated significant gains in object recognition accuracy with increasingly deep hierarchical architectures. But it is unclear how well these models account for human visual decisions and what they may reveal about the underlying brain processes. #R##N#We have conducted a large-scale psychophysics study to assess the correlation between computational models and human participants on a rapid animal vs. non-animal categorization task. We considered visual representations of varying complexity by analyzing the output of different stages of processing in three state-of-the-art deep networks. We found that recognition accuracy increases with higher stages of visual processing (higher level stages indeed outperforming human participants on the same task) but that human decisions agree best with predictions from intermediate stages. #R##N#Overall, these results suggest that human participants may rely on visual features of intermediate complexity and that the complexity of visual representations afforded by modern deep network models may exceed those used by human participants during rapid categorization.

Connected through hyperedge HE-1 (induced by source paper "disentangling neural mechanisms for perceptual grouping"):
  with members:
    - "deep predictive coding network for object recognition"
    - "one shot segmentation in clutter"
    - "can recurrent neural networks warp time"
    - "sample efficient image segmentation through recurrence"
    - "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "dynamic routing between capsules"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "psyphy a psychophysics driven evaluation framework for visual recognition"):
  with members:
    - "can we still avoid automatic face detection"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "towards open set deep networks"
    - "a deeper look at dataset bias"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-3 (induced by source paper "measuring human perception to improve handwritten document transcription"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "visual psychophysics for making face recognition algorithms more explainable"
    - "towards knowledge discovery from the vatican secret archives in codice ratio episode 1 machine transcription of the manuscripts"
    - "language model supervision for handwriting recognition model adaptation"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "using human brain activity to guide machine learning"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning what and where to attend"):
  with members:
    - "differential attention for visual question answering"
    - "learn to pay attention"
    - "smoothgrad removing noise by adding noise"
    - "interpreting deep visual representations via network dissection"
    - "what are the visual features underlying human versus machine vision"
    - "stnet selective tuning of convolutional networks for object localization"
    - "residual attention network for image classification"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-5 (induced by source paper "visual psychophysics for making face recognition algorithms more explainable"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "how image degradations affect deep cnn based face recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "what are the visual features underlying human versus machine vision"):
  with members:
    - "adapting deep network features to capture psychological representations"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "the role of typicality in object classification improving the generalization capacity of convolutional neural networks"
    - "deep networks can resemble human feed forward vision in invariant object recognition"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)