human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  Abstract: Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and they promise to dramatically improve micro-communities (e.g., urban farms, hospitals), macro-communities (e.g., cities and metropolises), urban structures (e.g., smart homes and cars), and living structures (e.g., human bodies, synthetic genomes). The question that we address in this article pertains to designing these CPS systems to be resilient-from-the-ground-up, and through progressive learning, resilient-by-reaction. An optimally designed system is resilient to both unique attacks and recurrent attacks, the latter with a lower overhead. Overall, the notion of resilience can be thought of in the light of three main sources of lack of resilience, as follows: exogenous factors, such as natural variations and attack scenarios; mismatch between engineered designs and exogenous factors ranging from DDoS (distributed denial-of-service) attacks or other cybersecurity nightmares, so called "black swan" events, disabling critical services of the municipal electrical grids and other connected infrastructures, data breaches, and network failures; and the fragility of engineered designs themselves encompassing bugs, human-computer interactions (HCI), and the overall complexity of real-world systems. In the paper, our focus is on design and deployment innovations that are broadly applicable across a range of CPS application areas.

Includes members:
  Member "traditional and heavy tailed self regularization in neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data")
  Member "numerical verification of affine systems with up to a billion dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online monitoring for safe pedestrian vehicle interactions")
  Member "juliareach a toolbox for set based reachability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithms for verifying deep neural networks")
  Member "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data")
  Member "netadapt platform aware neural network adaptation for mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "formal security analysis of neural networks using symbolic intervals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robustness verification for transformers")
  Member "a dual approach to scalable verification of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robustness verification for transformers")
  Member "verisig verifying safety properties of hybrid systems with neural network controllers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "validation of image based neural network controllers through adaptive stress testing")

Question: Please tell me which class the source paper 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.CY(Computers and Society)