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: "deep neural networks predict hierarchical spatio temporal cortical dynamics of human visual object recognition"
  Abstract: The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.

Connected through hyperedge HE-1 (induced by source paper "brain like object recognition with high performing shallow recurrent anns"):
  with members:
    - "do better imagenet models transfer better"
    - "exploring the limits of weakly supervised pretraining"
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "progressive neural architecture search"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "residual connections encourage iterative inference"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"):
  with members:
    - "learning word like units from joint audio visual analysis"
    - "english conversational telephone speech recognition by humans and machines"
    - "the zero resource speech challenge 2017"
    - "are words easier to learn from infant than adult directed speech a quantitative corpus based investigation"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "temporally coherent 4d reconstruction of complex dynamic scenes"
    - "building machines that learn and think like people"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "learning to attend in a brain inspired deep neural network"):
  with members:
    - "computational models bottom up and top down aspects"
    - "show attend and tell neural image caption generation with visual attention"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

Connected through hyperedge HE-4 (induced by source paper "on the selective and invariant representation of dcnn for high resolution remote sensing image recognition"):
  with members:
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "learning multi scale deep features for high resolution satellite image classification"
    - "understanding neural networks through deep visualization"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "convergent learning do different neural networks learn the same representations"
    - "understanding intra class knowledge inside cnn"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "using human brain activity to guide machine learning"):
  with members:
    - "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"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "transfer entropy based feedback improves performance in artificial neural networks"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "all you need is a good init"
    - "deep learning in neural networks"
    - "towards biologically plausible deep learning"
    - "deep residual learning for image recognition"
    - "striving for simplicity the all convolutional net"

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)