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 "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"):
  Abstract: The problem of monotonicity testing over the hypergrid and its special case, the hypercube, is a classic, well-studied, yet unsolved question in property testing. We are given query access to $f:[k]^n \mapsto \R$ (for some ordered range $\R$). The hypergrid/cube has a natural partial order given by coordinate-wise ordering, denoted by $\prec$. A function is \emph{monotone} if for all pairs $x \prec y$, $f(x) \leq f(y)$. The distance to monotonicity, $\eps_f$, is the minimum fraction of values of $f$ that need to be changed to make $f$ monotone. #R##N#For $k=2$ (the boolean hypercube), the usual tester is the \emph{edge tester}, which checks monotonicity on adjacent pairs of domain points. It is known that the edge tester using $O(\eps^{-1}n\log|\R|)$ samples can distinguish a monotone function from one where $\eps_f > \eps$. On the other hand, the best lower bound for monotonicity testing over the hypercube is $\min(|\R|^2,n)$. This leaves a quadratic gap in our knowledge, since $|\R|$ can be $2^n$. We resolve this long standing open problem and prove that $O(n/\eps)$ samples suffice for the edge tester. For hypergrids, known testers require $O(\eps^{-1}n\log k\log |\R|)$ samples, while the best known (non-adaptive) lower bound is $\Omega(\eps^{-1} n\log k)$. We give a (non-adaptive) monotonicity tester for hypergrids running in $O(\eps^{-1} n\log k)$ time. #R##N#Our techniques lead to optimal property testers (with the same running time) for the natural \emph{Lipschitz property} on hypercubes and hypergrids. (A $c$-Lipschitz function is one where $|f(x) - f(y)| \leq c\|x-y\|_1$.) In fact, we give a general unified proof for $O(\eps^{-1}n\log k)$-query testers for a class of "bounded-derivative" properties, a class containing both monotonicity and Lipschitz.

Includes members:
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "transitive closure spanners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient and simple algorithms for fault tolerant spanners")

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.DM(Discrete Mathematics)