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: "an adaptivity hierarchy theorem for property testing"
  Abstract: Adaptivity is known to play a crucial role in property testing. In particular, there exist properties for which there is an exponential gap between the power of \emph{adaptive} testing algorithms, wherein each query may be determined by the answers received to prior queries, and their \emph{non-adaptive} counterparts, in which all queries are independent of answers obtained from previous queries. #R##N#In this work, we investigate the role of adaptivity in property testing at a finer level. We first quantify the degree of adaptivity of a testing algorithm by considering the number of "rounds of adaptivity" it uses. More accurately, we say that a tester is $k$-(round) adaptive if it makes queries in $k+1$ rounds, where the queries in the $i$'th round may depend on the answers obtained in the previous $i-1$ rounds. Then, we ask the following question: #R##N#Does the power of testing algorithms smoothly grow with the number of rounds of adaptivity? #R##N#We provide a positive answer to the foregoing question by proving an adaptivity hierarchy theorem for property testing. Specifically, our main result shows that for every $n\in \mathbb{N}$ and $0 \le k \le n^{0.99}$ there exists a property $\mathcal{P}_{n,k}$ of functions for which (1) there exists a $k$-adaptive tester for $\mathcal{P}_{n,k}$ with query complexity $\tilde{O}(k)$, yet (2) any $(k-1)$-adaptive tester for $\mathcal{P}_{n,k}$ must make $\Omega(n)$ queries. In addition, we show that such a qualitative adaptivity hierarchy can be witnessed for testing natural properties of graphs.

Connected through hyperedge HE-1 (induced by source paper "noisy source location on a line"):
  with members:
    - "deterministic and probabilistic binary search in graphs"
    - "the capacity of adaptive group testing"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-2 (induced by source paper "a lower bound for relaxed locally decodable codes"):
  with members:
    - "relaxed locally correctable codes in computationally bounded channels"
    - "lower bounds for 2 query lccs over large alphabet"
    - "trading query complexity for sample based testing and multi testing scalability"
    - "efficient and error correcting data structures for membership and polynomial evaluation"
    - "some applications of coding theory in computational complexity"

Connected through hyperedge HE-3 (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"
    - "settling the query complexity of non adaptive junta testing"
    - "parallel algorithms for select and partition with noisy comparisons"

Connected through hyperedge HE-4 (induced by source paper "an exponential speedup in parallel running time for submodular maximization without loss in approximation"):
  with members:
    - "settling the query complexity of non adaptive junta 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-5 (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"
    - "settling the query complexity of non adaptive junta 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"

Connected through hyperedge HE-6 (induced by source paper "boolean unateness testing with widetilde o n 3 4 adaptive queries"):
  with members:
    - "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 widetilde o n non adaptive tester for unateness"
    - "a polynomial lower bound for testing monotonicity"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"

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.DS(Data Structures and Algorithms)