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: "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
  Abstract: We give a nearly optimal sublinear-time algorithm for approximating the size of a minimum vertex cover in a graph G. The algorithm may query the degree deg(v) of any vertex v of its choice, and for each 1 <= i <= deg(v), it may ask for the i-th neighbor of v. Letting VC_opt(G) denote the minimum size of vertex cover in G, the algorithm outputs, with high constant success probability, an estimate VC_estimate(G) such that VC_opt(G) <= VC_estimate(G) <= 2 * VC_opt(G) + epsilon*n, where epsilon is a given additive approximation parameter. We refer to such an estimate as a (2,epsilon)-estimate. The query complexity and running time of the algorithm are ~O(avg_deg * poly(1/epsilon)), where avg_deg denotes the average vertex degree in the graph. The best previously known sublinear algorithm, of Yoshida et al. (STOC 2009), has query complexity and running time O(d^4/epsilon^2), where d is the maximum degree in the graph. Given the lower bound of Omega(avg_deg) (for constant epsilon) for obtaining such an estimate (with any constant multiplicative factor) due to Parnas and Ron (TCS 2007), our result is nearly optimal. #R##N#In the case that the graph is dense, that is, the number of edges is Theta(n^2), we consider another model, in which the algorithm may ask, for any pair of vertices u and v, whether there is an edge between u and v. We show how to adapt the algorithm that uses neighbor queries to this model and obtain an algorithm that outputs a (2,epsilon)-estimate of the size of a minimum vertex cover whose query complexity and running time are ~O(n) * poly(1/epsilon).

Connected through hyperedge HE-1 (induced by source paper "complexity and efficient algorithms for data inconsistency evaluating and repairing"):
  with members:
    - "a formal framework for probabilistic unclean databases"
    - "computing optimal repairs for functional dependencies"
    - "on the relative trust between inconsistent data and inaccurate constraints"
    - "minimal change integrity maintenance using tuple deletions"

Connected through hyperedge HE-2 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples"):
  with members:
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "extractor based time space lower bounds for learning"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"

Connected through hyperedge HE-3 (induced by source paper "quantum inspired classical sublinear time algorithm for solving low rank semidefinite programming via sampling approaches"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "approximate nearest neighbor search in high dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"

Connected through hyperedge HE-4 (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-5 (induced by source paper "constant time dynamic delta 1 coloring and weight approximation for minimum spanning forest dynamic algorithms meet property testing"):
  with members:
    - "testable properties in general graphs and random order streaming"
    - "improved dynamic graph coloring"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "dynamic algorithms for graph coloring"
    - "dynamic graph coloring"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal edge estimation with independent set queries"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "fiding forbidden minors in sublinear time a o n 1 2 o 1 query one sided tester for minor closed properties on bounded degree graphs"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time algorithms for counting star subgraphs with applications to join selectivity estimation"
    - "approximately counting triangles in sublinear time"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"

Connected through hyperedge HE-7 (induced by source paper "set cover in sub linear time"):
  with members:
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "randomized composable core sets for distributed submodular maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "analytical approach to parallel repetition"

Connected through hyperedge HE-8 (induced by source paper "a lower bound on cycle finding in sparse digraphs"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "fiding forbidden minors in sublinear time a o n 1 2 o 1 query one sided tester for minor closed properties on bounded degree graphs"
    - "edge estimation with independent set oracles"
    - "on approximating the number of k cliques in sublinear time"
    - "approximately counting triangles in sublinear time"
    - "property testing in sparse directed graphs 3 star freeness and connectivity"

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)