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 "l_p testers for bounded derivative properties on product distributions"):
  Abstract: We consider the problem of $L_p$-testing of class of bounded derivative properties over hypergrid domain with points distributed according to some product distribution. This class includes monotonicity, the Lipschitz property, $(\alpha,\beta)$-generalized Lipschitz and many more properties. Previous results for $L_p$ testing on $[n]^d$ for this class were known for monotonicity and $c$-Lipschitz properties over uniformly distributed domains. \medskip #R##N#Our results imply testers that give the same upper bound for arbitrary product distributions as the hitherto known testers, which use uniformly randomly chosen samples from $[n]^d$, for monotonicity and Lipschitz testing. Also, our testers are \emph{optimal} for a large class of bounded derivative properties, that includes $(\alpha, \beta)$-generalized Lipschitz property, over uniform distributions. Infact, each edge in $[n]^d$ is allowed to have it's own left and right Lipschitz constants. The time complexity is \emph{same} for arbitrary product distributions.

Includes members:
  Member "optimal bounds on approximation of submodular and xos functions by juntas":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "an optimal lower bound for monotonicity testing over hypergrids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "optimal adaptive detection of monotone patterns")
  Member "testing closeness of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "transitive closure spanners":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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.DS(Data Structures and Algorithms)