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: "quantitative projection coverage for testing ml enabled autonomous systems"
  Abstract: Systematically testing models learned from neural networks remains a crucial unsolved barrier to successfully justify safety for autonomous vehicles engineered using data-driven approach. We propose quantitative k-projection coverage as a metric to mediate combinatorial explosion while guiding the data sampling process. By assuming that domain experts propose largely independent environment conditions and by associating elements in each condition with weights, the product of these conditions forms scenarios, and one may interpret weights associated with each equivalence class as relative importance. Achieving full k-projection coverage requires that the data set, when being projected to the hyperplane formed by arbitrarily selected k-conditions, covers each class with number of data points no less than the associated weight. For the general case where scenario composition is constrained by rules, precisely computing k-projection coverage remains in NP. In terms of finding minimum test cases to achieve full coverage, we present theoretical complexity for important sub-cases and an encoding to 0-1 integer programming. We have implemented a research prototype that generates test cases for a visual object defection unit in automated driving, demonstrating the technological feasibility of our proposed coverage criterion.

Connected through hyperedge HE-1 (induced by source paper "engineering problems in machine learning systems"):
  with members:
    - "improving transparency of deep neural inference process"
    - "towards dependability metrics for neural networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "runtime monitoring neuron activation patterns"
    - "towards proving the adversarial robustness of deep neural networks"
    - "maximum resilience of artificial neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "an analysis of iso 26262 using machine learning safely in automotive software"

Connected through hyperedge HE-2 (induced by source paper "towards robust direct perception networks for automated driving"):
  with members:
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "conditional affordance learning for driving in urban environments"
    - "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 "nn dependability kit engineering neural networks for safety critical autonomous driving systems"):
  with members:
    - "scenic a language for scenario specification and scene generation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a survey of safety and trustworthiness of deep neural networks"):
  with members:
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "testing deep neural networks"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "a formalization of robustness for deep neural networks"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of neural network controlled autonomous systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "nn dependability kit engineering neural networks for safety critical systems"):
  with members:
    - "scenic a language for scenario specification and scene generation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "towards dependability metrics for neural networks"):
  with members:
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "neural networks for safety critical applications challenges experiments and perspectives"
    - "maximum resilience of artificial neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "visualizing and understanding convolutional networks"
    - "intriguing properties of neural 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.SE(Software Engineering)