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 "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"):
  Abstract: We introduce a new model for testing graph properties which we call the \emph{rejection sampling model}. We show that testing bipartiteness of $n$-nodes graphs using rejection sampling queries requires complexity $\widetilde{\Omega}(n^2)$. Via reductions from the rejection sampling model, we give three new lower bounds for tolerant testing of Boolean functions of the form $f\colon\{0,1\}^n\to \{0,1\}$: #R##N#$\bullet$Tolerant $k$-junta testing with \emph{non-adaptive} queries requires $\widetilde{\Omega}(k^2)$ queries. #R##N#$\bullet$Tolerant unateness testing requires $\widetilde{\Omega}(n)$ queries. #R##N#$\bullet$Tolerant unateness testing with \emph{non-adaptive} queries requires $\widetilde{\Omega}(n^{3/2})$ queries. #R##N#Given the $\widetilde{O}(k^{3/2})$-query non-adaptive junta tester of Blais \cite{B08}, we conclude that non-adaptive tolerant junta testing requires more queries than non-tolerant junta testing. In addition, given the $\widetilde{O}(n^{3/4})$-query unateness tester of Chen, Waingarten, and Xie \cite{CWX17b} and the $\widetilde{O}(n)$-query non-adaptive unateness tester of Baleshzar, Chakrabarty, Pallavoor, Raskhodnikova, and Seshadhri \cite{BCPRS17}, we conclude that tolerant unateness testing requires more queries than non-tolerant unateness testing, in both adaptive and non-adaptive settings. These lower bounds provide the first separation between tolerant and non-tolerant testing for a natural property of Boolean functions.

Includes members:
  Member "a lower bound for nonadaptive one sided error testing of unateness of boolean functions over the hypercube":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "testing unateness nearly optimally")
  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 "settling the query complexity of non adaptive junta testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "approximating the distance to monotonicity of boolean functions")
  Member "a widetilde o n non adaptive tester for unateness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "almost optimal testers for concise representations")
  Member "testing probability distributions using conditional samples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")
  Member "on the power of conditional samples in distribution testing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning")

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.CC(Computational Complexity)