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: "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
  Abstract: We prove a lower bound of $\Omega(n^{1/2 - c})$, for all $c>0$, on the query complexity of (two-sided error) non-adaptive algorithms for testing whether an $n$-variable Boolean function is monotone versus constant-far from monotone. This improves a $\tilde{\Omega}(n^{1/5})$ lower bound for the same problem that was recently given in [CST14] and is very close to $\Omega(n^{1/2})$, which we conjecture is the optimal lower bound for this model.

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"
    - "new algorithms and lower bounds for monotonicity testing"

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"
    - "new algorithms and lower bounds for monotonicity testing"

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"
    - "new algorithms and lower bounds for monotonicity testing"
    - "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 "kruskal katona for convex sets with applications"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "analysis of boolean functions"
    - "an invariance principle for polytopes"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "average sensitivity and noise sensitivity of polynomial threshold functions"

Connected through hyperedge HE-7 (induced by source paper "distribution free junta testing"):
  with members:
    - "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"
    - "new algorithms and lower bounds for monotonicity testing"
    - "a o n monotonicity tester for boolean functions over the hypercube"
    - "local correction of juntas"

Connected through hyperedge HE-8 (induced by source paper "adaptive lower bound for testing monotonicity on the line"):
  with members:
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"
    - "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"

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)