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 "efficient detection and quantification of timing leaks with neural networks"):
  Abstract: Detection and quantification of information leaks through timing side channels are important to guarantee confidentiality. Although static analysis remains the prevalent approach for detecting timing side channels, it is computationally challenging for real-world applications. In addition, the detection techniques are usually restricted to 'yes' or 'no' answers. In practice, real-world applications may need to leak information about the secret. Therefore, quantification techniques are necessary to evaluate the resulting threats of information leaks. Since both problems are very difficult or impossible for static analysis techniques, we propose a dynamic analysis method. Our novel approach is to split the problem into two tasks. First, we learn a timing model of the program as a neural network. Second, we analyze the neural network to quantify information leaks. As demonstrated in our experiments, both of these tasks are feasible in practice --- making the approach a significant improvement over the state-of-the-art side channel detectors and quantifiers. Our key technical contributions are (a) a neural network architecture that enables side channel discovery and (b) an MILP-based algorithm to estimate the side-channel strength. On a set of micro-benchmarks and real-world applications, we show that neural network models learn timing behaviors of programs with thousands of methods. We also show that neural networks with thousands of neurons can be efficiently analyzed to detect and quantify information leaks through timing side channels.

Includes members:
  Member "mitigating power side channels during compilation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "specusym speculative symbolic execution for cache timing leak detection")
  Member "eliminating timing side channel leaks using program repair":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a compiler assisted scheduler for detecting and mitigating cache based side channel attacks")
  Member "adversarial symbolic execution for detecting concurrency related cache timing leaks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "specusym speculative symbolic execution for cache timing leak detection")
  Member "data driven debugging for functional side channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "statistical model checking for hyperproperties")
  Member "vuldeepecker a deep learning based system for vulnerability detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "canal a cache timing analysis framework via llvm transformation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "specusym speculative symbolic execution for cache timing leak detection")
  Member "discriminating traces with time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hotfuzz discovering algorithmic denial of service vulnerabilities through guided micro fuzzing")
  Member "differential performance debugging with discriminant regression trees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "quantitative mitigation of timing side channels")

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.CR(Cryptography and Security)