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 "hardness of bounded distance decoding on lattices in ell_p norms"):
  Abstract: $ \newcommand{\Z}{\mathbb{Z}} \newcommand{\eps}{\varepsilon} \newcommand{\cc}[1]{\mathsf{#1}} \newcommand{\NP}{\cc{NP}} \newcommand{\problem}[1]{\mathrm{#1}} \newcommand{\BDD}{\problem{BDD}} #R##N#$Bounded Distance Decoding $\BDD_{p,\alpha}$ is the problem of decoding a lattice when the target point is promised to be within an $\alpha$ factor of the minimum distance of the lattice, in the $\ell_{p}$ norm. We prove that $\BDD_{p, \alpha}$ is $\NP$-hard under randomized reductions where $\alpha \to 1/2$ as $p \to \infty$ (and for $\alpha=1/2$ when $p=\infty$), thereby showing the hardness of decoding for distances approaching the unique-decoding radius for large $p$. We also show fine-grained hardness for $\BDD_{p,\alpha}$. For example, we prove that for all $p \in [1,\infty) \setminus 2\Z$ and constants $C > 1, \eps > 0$, there is no $2^{(1-\eps)n/C}$-time algorithm for $\BDD_{p,\alpha}$ for some constant $\alpha$ (which approaches $1/2$ as $p \to \infty$), assuming the randomized Strong Exponential Time Hypothesis (SETH). Moreover, essentially all of our results also hold (under analogous non-uniform assumptions) for $\BDD$ with preprocessing, in which unbounded precomputation can be applied to the lattice before the target is available. #R##N#Compared to prior work on the hardness of $\BDD_{p,\alpha}$ by Liu, Lyubashevsky, and Micciancio (APPROX-RANDOM 2008), our results improve the values of $\alpha$ for which the problem is known to be $\NP$-hard for all $p > p_1 \approx 4.2773$, and give the very first fine-grained hardness for $\BDD$ (in any norm). Our reductions rely on a special family of "locally dense" lattices in $\ell_{p}$ norms, which we construct by modifying the integer-lattice sparsification technique of Aggarwal and Stephens-Davidowitz (STOC 2018).

Includes members:
  Member "fine grained hardness of cvp p everything that we can prove and nothing else":
    also connected through other hyperedges:
      (none)
  Member "gap s eth hardness of svp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tight running time lower bounds for strong inapproximability of maximum k coverage unique set cover and related problems via t wise agreement testing theorem")
  Member "on the quantitative hardness of cvp":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster provable sieving algorithms for the shortest vector problem and the closest vector problem on lattices in 120001 p norm")
  Member "discrete gaussian sampling reduces to cvp and svp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved provable algorithms for the shortest vector problem via bounded distance decoding")
  Member "solving the closest vector problem in 2 n time the discrete gaussian strikes again":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a time distance trade off for gdd with preprocessing instantiating the dlw heuristic")
  Member "on the closest vector problem with a distance guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a time distance trade off for gdd with preprocessing instantiating the dlw heuristic")
  Member "tensor based hardness of the shortest vector problem to within almost polynomial factors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved provable algorithms for the shortest vector problem via bounded distance decoding")

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.CC(Computational Complexity)