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: "learning visual biases from human imagination"
  Abstract: Although the human visual system can recognize many concepts under challenging conditions, it still has some biases. In this paper, we investigate whether we can extract these biases and transfer them into a machine recognition system. We introduce a novel method that, inspired by well-known tools in human psychophysics, estimates the biases that the human visual system might use for recognition, but in computer vision feature spaces. Our experiments are surprising, and suggest that classifiers from the human visual system can be transferred into a machine with some success. Since these classifiers seem to capture favorable biases in the human visual system, we further present an SVM formulation that constrains the orientation of the SVM hyperplane to agree with the bias from human visual system. Our results suggest that transferring this human bias into machines may help object recognition systems generalize across datasets and perform better when very little training data is available.

Connected through hyperedge HE-1 (induced by source paper "lift the flap what where and when for context reasoning"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "finding any waldo with zero shot invariant and efficient visual search"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning scene gist with convolutional neural networks to improve object recognition"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "scene graph generation by iterative message passing"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "white noise analysis of neural networks"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "understanding neural networks via feature visualization a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "using human brain activity to guide machine learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-3 (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"
    - "how deep is the feature analysis underlying rapid visual categorization"
    - "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"

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 "learning from web data the benefit of unsupervised object localization"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "cascade r cnn delving into high quality object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "exploiting web images for dataset construction a domain robust approach"
    - "snn stacked neural networks"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "yolo9000 better faster stronger"
    - "learning to segment object candidates"

Connected through hyperedge HE-6 (induced by source paper "capturing human category representations by sampling in deep feature spaces"):
  with members:
    - "adapting deep network features to capture psychological representations"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "using human brain activity to guide machine learning"):
  with members:
    - "deep neural networks predict hierarchical spatio temporal cortical dynamics of human visual object recognition"
    - "deep learning human mind for automated visual classification"
    - "building machines that learn and think like people"
    - "towards open world recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "draw a recurrent neural network for image generation"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (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"
    - "how deep is the feature analysis underlying rapid visual categorization"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

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)