human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "new algorithms and lower bounds for monotonicity testing"
  Abstract: We consider the problem of testing whether an unknown Boolean function $f$ is monotone versus $\epsilon$-far from every monotone function. The two main results of this paper are a new lower bound and a new algorithm for this well-studied problem. #R##N#Lower bound: We prove an $\tilde{\Omega}(n^{1/5})$ lower bound on the query complexity of any non-adaptive two-sided error algorithm for testing whether an unknown Boolean function $f$ is monotone versus constant-far from monotone. This gives an exponential improvement on the previous lower bound of $\Omega(\log n)$ due to Fischer et al. [FLN+02]. We show that the same lower bound holds for monotonicity testing of Boolean-valued functions over hypergrid domains $\{1,\ldots,m\}^n$ for all $m\ge 2$. #R##N#Upper bound: We give an $\tilde{O}(n^{5/6})\text{poly}(1/\epsilon)$-query algorithm that tests whether an unknown Boolean function $f$ is monotone versus $\epsilon$-far from monotone. Our algorithm, which is non-adaptive and makes one-sided error, is a modified version of the algorithm of Chakrabarty and Seshadhri [CS13a], which makes $\tilde{O}(n^{7/8})\text{poly}(1/\epsilon)$ queries.

Connected through hyperedge HE-1 (induced by source paper "almost optimal testers for concise representations"):
  with members:
    - "almost optimal distribution free junta testing"
    - "distribution free junta testing"
    - "near log convexity of measured heat in discrete time and consequences"
    - "exact learning from an honest teacher that answers membership queries"
    - "exact learning of juntas from membership queries"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "testing unateness of real valued functions"

Connected through hyperedge HE-2 (induced by source paper "optimal adaptive detection of monotone patterns"):
  with members:
    - "finding and counting permutations via csps"
    - "finding monotone patterns in sublinear time"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-3 (induced by source paper "approximating the distance to monotonicity of boolean functions"):
  with members:
    - "testing unateness nearly optimally"
    - "junta correlation is testable"
    - "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-4 (induced by source paper "finding monotone patterns in sublinear time"):
  with members:
    - "faster and simpler algorithms for finding large patterns in permutations"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "finding small patterns in permutations in linear time"

Connected through hyperedge HE-5 (induced by source paper "testing unateness nearly optimally"):
  with members:
    - "distribution free junta testing"
    - "adaptive boolean monotonicity testing in total influence time"
    - "a lower bound for nonadaptive one sided error testing of unateness of boolean functions over the hypercube"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "testing unateness of real valued functions"
    - "a polynomial lower bound for testing monotonicity"
    - "tight bounds for the distribution free testing of monotone conjunctions"

Connected through hyperedge HE-6 (induced by source paper "almost optimal distribution free junta testing"):
  with members:
    - "near log convexity of measured heat in discrete time and consequences"
    - "exact learning of juntas from membership queries"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "testing unateness of real valued functions"
    - "a polynomial lower bound for testing monotonicity"
    - "tight bounds for the distribution free testing of monotone conjunctions"
    - "a o n monotonicity tester for boolean functions over the hypercube"

Connected through hyperedge HE-7 (induced by source paper "approximating the noise sensitivity of a monotone boolean function"):
  with members:
    - "a polynomial lower bound for testing monotonicity"
    - "analysis of boolean functions"
    - "the average sensitivity of an intersection of half spaces"
    - "active property testing"
    - "submodular functions are noise stable"
    - "average sensitivity and noise sensitivity of polynomial threshold functions"

Connected through hyperedge HE-8 (induced by source paper "estimating learnability in the sublinear data regime"):
  with members:
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "spectrum estimation from samples"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "the power of localization for efficiently learning linear separators with noise"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "active property testing"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"

Question: Please tell me which class the center node 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)