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 "erasure resilient property testing"):
  Abstract: Property testers form an important class of sublinear algorithms. In the standard property testing model, an algorithm accesses the input function via an oracle that returns function values at all queried domain points. In many realistic situations, the oracle may be unable to reveal the function values at some domain points due to privacy concerns, or when some of the values get erased by mistake or by an adversary. #R##N#We initiate a study of property testers that are resilient to the presence of adversarially erased function values. An alpha-erasure-resilient epsilon-tester for a property P is given parameters alpha, epsilon in (0,1), along with oracle access to a function f such that at most an alpha fraction of the function values have been erased. The tester does not know whether a point is erased unless it queries that point. The tester has to accept with high probability if there is a way to assign values to the erased points such that the resulting function satisfies P. It has to reject with high probability if, for all assignments of values to the erased points, the resulting function has to be changed in at least an epsilon-fraction of the nonerased domain points to satisfy P. #R##N#We design erasure-resilient property testers for a large class of properties. For some properties, it is possible to obtain erasure-resilient testers by using standard testers as a black box. But there are more challenging properties for which all known testers rely on querying a specific point. If this point is erased, all these testers break. We give efficient erasure-resilient testers for several classes of such properties including monotonicity, the Lipschitz property, and convexity. Finally, we describe a property that can be epsilon-tested with O(1/epsilon) queries in the standard model, whereas testing it in the erasure-resilient model requires number of queries polynomial in the input size.

Includes members:
  Member "property testing on product distributions optimal testers for bounded derivative properties":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain reduction for monotonicity testing a o d tester for boolean functions in d dimensions")
  Member "estimating the longest increasing sequence in polylogarithmic time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal adaptive detection of monotone patterns")
  Member "an optimal lower bound for monotonicity testing over hypergrids":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal adaptive detection of monotone patterns")
  Member "active property testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improvability through semi supervised learning a survey of theoretical results")
  Member "testing closeness of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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)