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 "domain reduction for monotonicity testing a o d tester for boolean functions in d dimensions"):
  Abstract: We describe a $\tilde{O}(d^{5/6})$-query monotonicity tester for Boolean functions $f:[n]^d \to \{0,1\}$ on the $n$-hypergrid. This is the first $o(d)$ monotonicity tester with query complexity independent of $n$. Motivated by this independence of $n$, we initiate the study of monotonicity testing of measurable Boolean functions $f:\mathbb{R}^d \to \{0,1\}$ over the continuous domain, where the distance is measured with respect to a product distribution over $\mathbb{R}^d$. We give a $\tilde{O}(d^{5/6})$-query monotonicity tester for such functions. #R##N#Our main technical result is a domain reduction theorem for monotonicity. For any function $f:[n]^d \to \{0,1\}$, let $\epsilon_f$ be its distance to monotonicity. Consider the restriction $\hat{f}$ of the function on a random $[k]^d$ sub-hypergrid of the original domain. We show that for $k = \text{poly}(d/\epsilon)$, the expected distance of the restriction is $\mathbb{E}[\epsilon_{\hat{f}}] = \Omega(\epsilon_f)$. Previously, such a result was only known for $d=1$ (Berman-Raskhodnikova-Yaroslavtsev, STOC 2014). Our result for testing Boolean functions over $[n]^d$ then follows by applying the $d^{5/6}\cdot \text{poly}(1/\epsilon,\log n, \log d)$-query hypergrid tester of Black-Chakrabarty-Seshadhri (SODA 2018). #R##N#To obtain the result for testing Boolean functions over $\mathbb{R}^d$, we use standard measure theoretic tools to reduce monotonicity testing of a measurable function $f$ to monotonicity testing of a discretized version of $f$ over a hypergrid domain $[N]^d$ for large, but finite, $N$ (that may depend on $f$). The independence of $N$ in the hypergrid tester is crucial to getting the final tester over $\mathbb{R}^d$.

Includes members:
  Member "is your function low dimensional":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distribution free testing of linear functions on r n")
  Member "beyond talagrand functions new lower bounds for testing monotonicity and unateness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "almost optimal testers for concise representations")
  Member "a polynomial lower bound for testing monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "almost optimal testers for concise representations")
  Member "property testing on product distributions optimal testers for bounded derivative properties":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal unateness testers for real valued functions adaptivity helps")
  Member "new algorithms and lower bounds for monotonicity testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "almost optimal testers for concise representations")
  Member "an optimal lower bound for monotonicity testing over hypergrids":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal adaptive detection of monotone patterns")
  Member "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal adaptive detection of monotone patterns")

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)