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: "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
  Abstract: Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "adversarial filters of dataset biases"):
  with members:
    - "dataset distillation"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "repair removing representation bias by dataset resampling"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"

Connected through hyperedge HE-3 (induced by source paper "g odel s sentence is an adversarial example but unsolvable"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "adversarial examples are a natural consequence of test error in noise"
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial attacks on deep learning models in natural language processing a survey"

Connected through hyperedge HE-4 (induced by source paper "safety concerns and mitigation approaches regarding the use of deep learning in safety critical perception tasks"):
  with members:
    - "hardening of artificial neural networks for use in safety critical applications a mapping study"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "on physical adversarial patches for object detection"
    - "scaling provable adversarial defenses"
    - "adversarial patch"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-5 (induced by source paper "challenges and prospects in vision and language research"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vqd visual query detection in natural scenes"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "do explanations make vqa models more predictable to a human"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-6 (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-7 (induced by source paper "fine grained synthesis of unrestricted adversarial examples"):
  with members:
    - "neural puppet generative layered cartoon characters"
    - "certified adversarial robustness via randomized smoothing"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "spatially transformed adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-8 (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"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "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"

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)