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: "deepbillboard systematic physical world testing of autonomous driving systems"
  Abstract: Deep Neural Networks (DNNs) have been widely applied in many autonomous systems such as autonomous driving. Recently, DNN testing has been intensively studied to automatically generate adversarial examples, which inject small-magnitude perturbations into inputs to test DNNs under extreme situations. While existing testing techniques prove to be effective, they mostly focus on generating digital adversarial perturbations (particularly for autonomous driving), e.g., changing image pixels, which may never happen in physical world. There is a critical missing piece in the literature on autonomous driving testing: understanding and exploiting both digital and physical adversarial perturbation generation for impacting steering decisions. In this paper, we present DeepBillboard, a systematic physical-world testing approach targeting at a common and practical driving scenario: drive-by billboards. DeepBillboard is capable of generating a robust and resilient printable adversarial billboard, which works under dynamic changing driving conditions including viewing angle, distance, and lighting. The objective is to maximize the possibility, degree, and duration of the steering-angle errors of an autonomous vehicle driving by the generated adversarial billboard. We have extensively evaluated the efficacy and robustness of DeepBillboard through conducting both digital and physical-world experiments. Results show that DeepBillboard is effective for various steering models and scenes. Furthermore, DeepBillboard is sufficiently robust and resilient for generating physical-world adversarial billboard tests for real-world driving under various weather conditions. To the best of our knowledge, this is the first study demonstrating the possibility of generating realistic and continuous physical-world tests for practical autonomous driving systems.

Connected through hyperedge HE-1 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "ernie enhanced representation through knowledge integration"
    - "a neural model for generating natural language summaries of program subroutines"
    - "characterizing and detecting cuda program bugs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "aroma code recommendation via structural code search"
    - "deep reinforcement learning for sequence to sequence models"
    - "active inductive logic programming for code search"

Connected through hyperedge HE-2 (induced by source paper "manifold based test generation for image classifiers"):
  with members:
    - "input prioritization for testing neural networks"
    - "diagnosing and enhancing vae models"
    - "machine learning testing survey landscapes and horizons"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "unsupervised image to image translation networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "physgan generating physical world resilient adversarial examples for autonomous driving"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "lavan localized and visible adversarial noise"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-4 (induced by source paper "physical adversarial textures that fool visual object tracking"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "end to end representation learning for correlation filter based tracking"
    - "underwater multi robot convoying using visual tracking by detection"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "detect to track and track to detect"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "comparing offline and online testing of deep neural networks an autonomous car case study"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "paracosm a language and tool for testing autonomous driving systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "guiding deep learning system testing using surprise adequacy"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "carla an open urban driving simulator"
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "deepxplore automated whitebox testing of deep learning systems"

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)