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 "approximating the distance to monotonicity of boolean functions"):
  Abstract: We design a nonadaptive algorithm that, given a Boolean function $f\colon \{0,1\}^n \to \{0,1\}$ which is $\alpha$-far from monotone, makes poly$(n, 1/\alpha)$ queries and returns an estimate that, with high probability, is an $\widetilde{O}(\sqrt{n})$-approximation to the distance of $f$ to monotonicity. Furthermore, we show that for any constant $\kappa > 0,$ approximating the distance to monotonicity up to $n^{1/2 - \kappa}$-factor requires $2^{n^\kappa}$ nonadaptive queries, thereby ruling out a poly$(n, 1/\alpha)$-query nonadaptive algorithm for such approximations. This answers a question of Seshadhri (Property Testing Review, 2014) for the case of nonadaptive algorithms. Approximating the distance to a property is closely related to tolerantly testing that property. Our lower bound stands in contrast to standard (non-tolerant) testing of monotonicity that can be done nonadaptively with $\widetilde{O}(\sqrt{n} / \varepsilon^2)$ queries. #R##N#We obtain our lower bound by proving an analogous bound for erasure-resilient testers. An $\alpha$-erasure-resilient tester for a desired property gets oracle access to a function that has at most an $\alpha$ fraction of values erased. The tester has to accept (with probability at least 2/3) if the erasures can be filled in to ensure that the resulting function has the property and to reject (with probability at least 2/3) if every completion of erasures results in a function that is $\varepsilon$-far from having the property. Our method yields the same lower bounds for unateness and being a $k$-junta. These lower bounds improve exponentially on the existing lower bounds for these properties.

Includes members:
  Member "testing unateness nearly optimally":
    also connected through other hyperedges:
      (none)
  Member "junta correlation is testable":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hard properties with very short pcpps and their applications")
  Member "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hard properties with very short pcpps and their applications")
  Member "beyond talagrand functions new lower bounds for testing monotonicity and unateness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "almost optimal testers for concise representations")
  Member "settling the query complexity of non adaptive junta testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "almost optimal testers for concise representations")
  Member "optimal unateness testers for real valued functions adaptivity helps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "testing unateness nearly optimally")
  Member "tolerant junta testing and the connection to submodular optimization and function isomorphism":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "boolean function monotonicity testing requires almost n 1 2 non adaptive queries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "almost optimal testers for concise representations")

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)