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: "scenic a language for scenario specification and scene generation"
  Abstract: We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a scene, a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.

Connected through hyperedge HE-1 (induced by source paper "sampling prediction matching examples in neural networks a probabilistic programming approach"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "on evaluating adversarial robustness"
    - "pyro deep universal probabilistic programming"
    - "deep learning for classical japanese literature"
    - "certified adversarial robustness with additive noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-2 (induced by source paper "nn dependability kit engineering neural networks for safety critical autonomous driving systems"):
  with members:
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "runtime monitoring neuron activation patterns"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "a programmatic and semantic approach to explaining and debuggingneural network based object detectors"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "smoothgrad removing noise by adding noise"
    - "carla an open urban driving simulator"
    - "interpreting deep visual representations via network dissection"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-4 (induced by source paper "verifai a toolkit for the design and analysis of artificial intelligence based systems"):
  with members:
    - "counterexample guided data augmentation"
    - "semantic adversarial deep learning"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "feature guided black box safety testing of deep neural networks"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "safe reinforcement learning via shielding"
    - "proximal policy optimization algorithms"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-5 (induced by source paper "nn dependability kit engineering neural networks for safety critical systems"):
  with members:
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "runtime monitoring neuron activation patterns"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "conditional affordance learning for driving in urban environments"
    - "concolic testing for deep neural networks"
    - "testing deep neural networks"
    - "quantitative projection coverage for testing ml enabled autonomous 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.PL(Programming Languages)