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: "visualizing deep convolutional neural networks using natural pre images"
  Abstract: Image representations, from SIFT and bag of visual words to convolutional neural networks (CNNs) are a crucial component of almost all computer vision systems. However, our understanding of them remains limited. In this paper we study several landmark representations, both shallow and deep, by a number of complementary visualization techniques. These visualizations are based on the concept of "natural pre-image", namely a natural-looking image whose representation has some notable property. We study in particular three such visualizations: inversion, in which the aim is to reconstruct an image from its representation, activation maximization, in which we search for patterns that maximally stimulate a representation component, and caricaturization, in which the visual patterns that a representation detects in an image are exaggerated. We pose these as a regularized energy-minimization framework and demonstrate its generality and effectiveness. In particular, we show that this method can invert representations such as HOG more accurately than recent alternatives while being applicable to CNNs too. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.

Connected through hyperedge HE-1 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "choose your neuron incorporating domain knowledge through neuron importance"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"
    - "analyzing the behavior of visual question answering models"

Connected through hyperedge HE-2 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"

Connected through hyperedge HE-3 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "yolact real time instance segmentation"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "deep learning for generic object detection a survey"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (induced by source paper "dreaming to distill data free knowledge transfer via deepinversion"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "relational knowledge distillation"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "variational information distillation for knowledge transfer"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"
    - "how good is my gan"

Connected through hyperedge HE-5 (induced by source paper "an analysis of pre training on object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "cornernet detecting objects as paired keypoints"
    - "do better imagenet models transfer better"
    - "taskonomy disentangling task transfer learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-6 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

Connected through hyperedge HE-7 (induced by source paper "fda feature disruptive attack"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "adversarial logit pairing"
    - "gray box adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"

Connected through hyperedge HE-8 (induced by source paper "finding and visualizing weaknesses of deep reinforcement learning agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial policies attacking deep reinforcement learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "establishing appropriate trust via critical states"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "sanity checks for saliency maps"
    - "understanding regularization to visualize convolutional neural networks"
    - "evaluating feature importance estimates"

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)