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: "adapting deep network features to capture psychological representations"
  Abstract: Deep neural networks have become increasingly successful at solving classic perception problems such as object recognition, semantic segmentation, and scene understanding, often reaching or surpassing human-level accuracy. This success is due in part to the ability of DNNs to learn useful representations of high-dimensional inputs, a problem that humans must also solve. We examine the relationship between the representations learned by these networks and human psychological representations recovered from similarity judgments. We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images. However, these features do not capture some qualitative distinctions that are a key part of human representations. To remedy this, we develop a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments.

Connected through hyperedge HE-1 (induced by source paper "high level perceptual similarity is enabled by learning diverse tasks"):
  with members:
    - "totally looks like how humans compare compared to machines"
    - "taskonomy disentangling task transfer learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "emergence of structured behaviors from curiosity based intrinsic motivation"
    - "learning to attend in a brain inspired deep neural network"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "humans can decipher adversarial images"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial vision challenge"
    - "adversarial patch"
    - "lavan localized and visible adversarial noise"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "synthesizing robust adversarial examples"
    - "the limitations of deep learning in adversarial settings"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "totally looks like how humans compare compared to machines"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "modeling human categorization of natural images using deep feature representations"
    - "recent advance in content based image retrieval a literature survey"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "understanding and mapping natural beauty"
    - "densely connected convolutional networks"
    - "understanding image virality"
    - "deep learning in neural networks"

Connected through hyperedge HE-4 (induced by source paper "capturing human category representations by sampling in deep feature spaces"):
  with members:
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning visual biases from human imagination"

Connected through hyperedge HE-5 (induced by source paper "learning hierarchical visual representations in deep neural networks using hierarchical linguistic labels"):
  with members:
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "what are the visual features underlying human versus machine vision"):
  with members:
    - "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"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "the incremental multiresolution matrix factorization algorithm"):
  with members:
    - "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"
    - "understanding neural networks through deep visualization"
    - "parallel mmf a multiresolution approach to matrix computation"
    - "inverting visual representations with convolutional networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-8 (induced by source paper "transfer of view manifold learning to similarity perception of novel objects"):
  with members:
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "unsupervised visual representation learning by context prediction"
    - "shapenet an information rich 3d model repository"
    - "single view to multi view reconstructing unseen views with a convolutional network"
    - "deep exemplar 2d 3d detection by adapting from real to rendered views"
    - "caffe convolutional architecture for fast feature embedding"
    - "fine grained visual categorization via multi stage metric learning"

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)