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: "psyphy a psychophysics driven evaluation framework for visual recognition"
  Abstract: By providing substantial amounts of data and standardized evaluation protocols, datasets in computer vision have helped fuel advances across all areas of visual recognition. But even in light of breakthrough results on recent benchmarks, it is still fair to ask if our recognition algorithms are doing as well as we think they are. The vision sciences at large make use of a very different evaluation regime known as Visual Psychophysics to study visual perception. Psychophysics is the quantitative examination of the relationships between controlled stimuli and the behavioral responses they elicit in experimental test subjects. Instead of using summary statistics to gauge performance, psychophysics directs us to construct item-response curves made up of individual stimulus responses to find perceptual thresholds, thus allowing one to identify the exact point at which a subject can no longer reliably recognize the stimulus class. In this article, we introduce a comprehensive evaluation framework for visual recognition models that is underpinned by this methodology. Over millions of procedurally rendered 3D scenes and 2D images, we compare the performance of well-known convolutional neural networks. Our results bring into question recent claims of human-like performance, and provide a path forward for correcting newly surfaced algorithmic deficiencies.

Connected through hyperedge HE-1 (induced by source paper "utilizing network properties to detect erroneous inputs"):
  with members:
    - "looking ahead anticipating pedestrians crossing with future frames prediction"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "selectivenet a deep neural network with an integrated reject option"
    - "improved adversarial robustness by reducing open space risk via tent activations"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "online normalization for training neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "when does label smoothing help"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "stand alone self attention in vision models"
    - "are perceptually aligned gradients a general property of robust classifiers"
    - "large scale adversarial representation learning"

Connected through hyperedge HE-4 (induced by source paper "enlightengan deep light enhancement without paired supervision"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "joint enhancement and denoising method via sequential decomposition"
    - "the relativistic discriminator a key element missing from standard gan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "scale recurrent network for deep image deblurring"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "measuring human perception to improve handwritten document transcription"):
  with members:
    - "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"
    - "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-6 (induced by source paper "the limits and potentials of deep learning for robotics"):
  with members:
    - "on calibration of modern neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "sparse representation based open set recognition"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-7 (induced by source paper "visual psychophysics for making face recognition algorithms more explainable"):
  with members:
    - "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"
    - "predicting first impressions with deep learning"

Connected through hyperedge HE-8 (induced by source paper "a neurobiological evaluation metric for neural network model search"):
  with members:
    - "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"
    - "progressive neural architecture search"

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)