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: "settling the query complexity of non adaptive junta testing"
  Abstract: We prove that any non-adaptive algorithm that tests whether an unknown Boolean function $f: \{0, 1\}^n\to \{0, 1\}$ is a $k$-junta or $\epsilon$-far from every $k$-junta must make $\widetilde{\Omega}(k^{3/2} / \epsilon)$ many queries for a wide range of parameters $k$ and $\epsilon$. Our result dramatically improves previous lower bounds from [BGSMdW13, STW15], and is essentially optimal given Blais's non-adaptive junta tester from [Blais08], which makes $\widetilde{O}(k^{3/2})/\epsilon$ queries. Combined with the adaptive tester of [Blais09] which makes $O(k\log k + k /\epsilon)$ queries, our result shows that adaptivity enables polynomial savings in query complexity for junta testing.

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"
    - "testing unateness of real valued functions"
    - "an widetilde o n queries adaptive tester for unateness"

Connected through hyperedge HE-2 (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"
    - "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"
    - "new algorithms and lower bounds for monotonicity testing"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "junta correlation is testable"):
  with members:
    - "distribution free junta testing"
    - "near log convexity of measured heat in discrete time and consequences"
    - "boolean function analysis meets stochastic optimization an approximation scheme for stochastic knapsack"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (induced by source paper "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the importance of communities for learning to influence"
    - "an adaptivity hierarchy theorem for property testing"
    - "parallel algorithms for select and partition with noisy comparisons"

Connected through hyperedge HE-6 (induced by source paper "an exponential speedup in parallel running time for submodular maximization without loss in approximation"):
  with members:
    - "an adaptivity hierarchy theorem for property testing"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "welfare maximization with limited interaction"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "lazier than lazy greedy"
    - "the non adaptive query complexity of testing k parities"

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"
    - "testing unateness of real valued functions"
    - "a polynomial lower bound for testing monotonicity"
    - "tight bounds for the distribution free testing of monotone conjunctions"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "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 "non monotone submodular maximization in exponentially fewer iterations"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "an adaptivity hierarchy theorem for property testing"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "constrained submodular maximization beyond 1 e"
    - "the non adaptive query complexity of testing k parities"
    - "on the power of adaptivity in sparse recovery"
    - "submodular maximization by simulated annealing"

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)