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 "noisy simon period finding"):
  Abstract: Let $f: \mathbb{F}_2^n \rightarrow \mathbb{F}_2^n$ be a Boolean function with period $\vec s$. It is well-known that Simon's algorithm finds $\vec s$ in time polynomial in $n$ on quantum devices that are capable of performing error-correction. However, today's quantum devices are inherently noisy, too limited for error correction, and Simon's algorithm is not error-tolerant. We show that even noisy quantum period finding computations lead to speedups in comparison to purely classical computations. More precisely, we implemented Simon's quantum period finding circuit on the $15$-qubit quantum device IBM Q 16 Melbourne. Our experiments show that with a certain probability $\tau(n)$ we measure erroneous vectors that are not orthogonal to $\vec s$. We propose new, simple, but very effective smoothing techniques to classically mitigate physical noise effects such as e.g. IBM Q's bias towards the $0$-qubit. After smoothing, our noisy quantum device provides us a statistical distribution that we can easily transform into an LPN instance with parameters $n$ and $\tau(n)$. Hence, in the noisy case we may not hope to find periods in time polynomial in $n$. However, we still obtain quantum advantage even for large errors $\tau(n)$ close to $\frac 1 2$. Thus, period finding does not necessarily require full quantum error correction capability.  Keywords: Noise-tolerant Simon period fnding, IBM Q 16, LPN algorithms, quantum advantage.

Includes members:
  Member "an improved bkw algorithm for lwe with applications to cryptography and lattices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algebraic aspects of solving ring lwe including ring based improvements in the blum kalai wasserman algorithm")
  Member "noise tolerant learning the parity problem and the statistical query model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization")

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)