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: "realistic adversarial examples in 3d meshes"
  Abstract: Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications and achieved increasing success. However, recent studies show that such machine learning models appear to be vulnerable against adversarial examples. So far adversarial examples have been heavily explored for 2D images, while few works have conducted to understand vulnerabilities of 3D objects which exist in real world, where 3D objects are projected to 2D domains by photo taking for different learning (recognition) tasks. In this paper, we consider adversarial behaviors in practical scenarios by manipulating the shape and texture of a given 3D mesh representation of an object. Our goal is to project the optimized "adversarial meshes" to 2D with a photorealistic renderer, and still able to mislead different machine learning models. Extensive experiments show that by generating unnoticeable 3D adversarial perturbation on shape or texture for a 3D mesh, the corresponding projected 2D instance can either lead classifiers to misclassify the victim object as an arbitrary malicious target, or hide any target object within the scene from object detectors. We conduct human studies to show that our optimized adversarial 3D perturbation is highly unnoticeable for human vision systems. In addition to the subtle perturbation for a given 3D mesh, we also propose to synthesize a realistic 3D mesh and put in a scene mimicking similar rendering conditions and therefore attack different machine learning models. In-depth analysis of transferability among various 3D renderers and vulnerable regions of meshes are provided to help better understand adversarial behaviors in real-world.

Connected through hyperedge HE-1 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "robust adversarial perturbation on deep proposal based models"

Connected through hyperedge HE-2 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "identifying model weakness with adversarial examiner"):
  with members:
    - "using videos to evaluate image model robustness"
    - "embodied visual recognition"
    - "meta sim learning to generate synthetic datasets"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "deep nets what have they ever done for vision"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-4 (induced by source paper "seeing isn t believing practical adversarial attack against object detectors"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "dup net denoiser and upsampler network for 3d adversarial point clouds defense"):
  with members:
    - "adversarial attack and defense on point sets"
    - "one pixel attack for fooling deep neural networks"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "generating 3d adversarial point clouds"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deep sets"

Connected through hyperedge HE-6 (induced by source paper "deflecting 3d adversarial point clouds through outlier guided removal"):
  with members:
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "generating 3d adversarial point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-7 (induced by source paper "data poisoning attack against unsupervised node embedding methods"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial attack on graph structured data"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "generating adversarial examples with adversarial networks"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial examples for evaluating reading comprehension systems"
    - "node2vec scalable feature learning for 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.CR(Cryptography and Security)