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: "on the lattice smoothing parameter problem"
  Abstract: The smoothing parameter $\eta_{\epsilon}(\mathcal{L})$ of a Euclidean lattice $\mathcal{L}$, introduced by Micciancio and Regev (FOCS'04; SICOMP'07), is (informally) the smallest amount of Gaussian noise that "smooths out" the discrete structure of $\mathcal{L}$ (up to error $\epsilon$). It plays a central role in the best known worst-case/average-case reductions for lattice problems, a wealth of lattice-based cryptographic constructions, and (implicitly) the tightest known transference theorems for fundamental lattice quantities. #R##N#In this work we initiate a study of the complexity of approximating the smoothing parameter to within a factor $\gamma$, denoted $\gamma$-${\rm GapSPP}$. We show that (for $\epsilon = 1/{\rm poly}(n)$): $(2+o(1))$-${\rm GapSPP} \in {\rm AM}$, via a Gaussian analogue of the classic Goldreich-Goldwasser protocol (STOC'98); $(1+o(1))$-${\rm GapSPP} \in {\rm coAM}$, via a careful application of the Goldwasser-Sipser (STOC'86) set size lower bound protocol to thin spherical shells; $(2+o(1))$-${\rm GapSPP} \in {\rm SZK} \subseteq {\rm AM} \cap {\rm coAM}$ (where ${\rm SZK}$ is the class of problems having statistical zero-knowledge proofs), by constructing a suitable instance-dependent commitment scheme (for a slightly worse $o(1)$-term); $(1+o(1))$-${\rm GapSPP}$ can be solved in deterministic $2^{O(n)} {\rm polylog}(1/\epsilon)$ time and $2^{O(n)}$ space. #R##N#As an application, we demonstrate a tighter worst-case to average-case reduction for basing cryptography on the worst-case hardness of the ${\rm GapSPP}$ problem, with $\tilde{O}(\sqrt{n})$ smaller approximation factor than the ${\rm GapSVP}$ problem. #R##N#Central to our results are two novel, and nearly tight, characterizations of the magnitude of discrete Gaussian sums.

Connected through hyperedge HE-1 (induced by source paper "awgn goodness is enough capacity achieving lattice codes based on dithered probabilistic shaping"):
  with members:
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "algebraic lattice codes achieve the capacity of the compound block fading channel"
    - "lda lattices without dithering achieve capacity on the gaussian channel"
    - "almost universal codes for fading wiretap channels"
    - "construction of capacity achieving lattice codes polar lattices"
    - "semantically secure lattice codes for the gaussian wiretap channel"

Connected through hyperedge HE-2 (induced by source paper "solving the shortest vector problem in 2 n time via discrete gaussian sampling"):
  with members:
    - "discrete gaussian sampling reduces to cvp and svp"
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "short paths on the voronoi graph and the closest vector problem with preprocessing"
    - "on the closest vector problem with a distance guarantee"

Connected through hyperedge HE-3 (induced by source paper "on the closest vector problem with a distance guarantee"):
  with members:
    - "tensor based hardness of the shortest vector problem to within almost polynomial factors"
    - "lattice sparsification and the approximate closest vector problem"

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.CC(Computational Complexity)