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: "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.

Connected through hyperedge HE-1 (induced by source paper "optimal adaptive detection of monotone patterns"):
  with members:
    - "finding and counting permutations via csps"
    - "finding monotone patterns in sublinear time"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-2 (induced by source paper "approximating the distance to monotonicity of boolean functions"):
  with members:
    - "testing unateness nearly optimally"
    - "junta correlation is testable"
    - "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-3 (induced by source paper "finding monotone patterns in sublinear time"):
  with members:
    - "faster and simpler algorithms for finding large patterns in permutations"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"

Connected through hyperedge HE-4 (induced by source paper "towards testing monotonicity of distributions over general posets"):
  with members:
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"
    - "a polynomial lower bound for testing monotonicity"
    - "testing shape restrictions of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "learning k modal distributions via testing"
    - "testing k modal distributions optimal algorithms via reductions"

Connected through hyperedge HE-5 (induced by source paper "testing convexity of functions over finite domains"):
  with members:
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "is submodularity testable"

Connected through hyperedge HE-6 (induced by source paper "domain reduction for monotonicity testing a o d tester for boolean functions in d dimensions"):
  with members:
    - "is your function low dimensional"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "property testing on product distributions optimal testers for bounded derivative properties"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"

Connected through hyperedge HE-7 (induced by source paper "adaptive lower bound for testing monotonicity on the line"):
  with members:
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"

Connected through hyperedge HE-8 (induced by source paper "adaptivity is exponentially powerful for testing monotonicity of halfspaces"):
  with members:
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "a polynomial lower bound for testing monotonicity"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"

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.DM(Discrete Mathematics)