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: "neural networks for safety critical applications challenges experiments and perspectives"
  Abstract: We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards. We apply the concept in a concrete case study in designing a high-way ANN-based motion predictor to guarantee safety properties such as impossibility for the ego vehicle to suggest moving to the right lane if there exists another vehicle on its right.

Connected through hyperedge HE-1 (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-2 (induced by source paper "quantitative projection coverage for testing ml enabled autonomous systems"):
  with members:
    - "concolic testing for deep neural networks"
    - "testing deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "maximum resilience of artificial neural networks"
    - "formal verification of piece wise linear feed forward neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-3 (induced by source paper "towards dependability metrics for neural networks"):
  with members:
    - "quantitative projection coverage for testing ml enabled autonomous systems"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "traceability of deep neural networks"):
  with members:
    - "towards dependability metrics for neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "an analysis of iso 26262 using machine learning safely in automotive software"
    - "ssd single shot multibox detector"
    - "concrete problems in ai safety"
    - "church a language for generative models"

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)