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: "using human brain activity to guide machine learning"
  Abstract: Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Connected through hyperedge HE-1 (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"
    - "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"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-2 (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"
    - "how deep is the feature analysis underlying rapid visual categorization"
    - "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-3 (induced by source paper "brain mediated transfer learning of convolutional neural networks"):
  with members:
    - "soundnet learning sound representations from unlabeled video"
    - "deep filter banks for texture recognition description and segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "deeppose human pose estimation via deep neural networks"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "teacher guided architecture search"):
  with members:
    - "adaptive stochastic natural gradient method for one shot neural architecture search"
    - "massively parallel video networks"
    - "regularized evolution for image classifier architecture search"
    - "irlas inverse reinforcement learning for architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-5 (induced by source paper "a neurobiological evaluation metric for neural network model search"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "visual psychophysics for making face recognition algorithms more explainable"
    - "totally looks like how humans compare compared to machines"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "generalisation in humans and deep neural networks"
    - "teacher guided architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-6 (induced by source paper "negative results in computer vision a perspective"):
  with members:
    - "understanding neural networks through deep visualization"
    - "spatial transformer networks"
    - "exploring nearest neighbor approaches for image captioning"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "learning visual biases from human imagination"
    - "visualizing and understanding convolutional networks"

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)