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: "k medians facility location and the chernoff wald bound"
  Abstract: The paper gives approximation algorithms for the k-medians and facility-location problems (both NP-hard). For k-medians, the algorithm returns a solution using at most ln(n+n/epsilon)k medians and having cost at most (1+epsilon) times the cost of the best solution that uses at most k medians. Here epsilon > 0 is an input to the algorithm. In comparison, the best previous algorithm (Jyh-Han Lin and Jeff Vitter, 1992) had a (1+1/epsilon)ln(n) term instead of the ln(n+n/epsilon) term in the performance guarantee. For facility location, the algorithm returns a solution of cost at most d+ln(n) k, provided there exists a solution of cost d+k where d is the assignment cost and k is the facility cost. In comparison, the best previous algorithm (Dorit Hochbaum, 1982) returned a solution of cost at most ln(n)(d+k). For both problems, the algorithms currently provide the best performance guarantee known for the general (non-metric) problems. #R##N#The paper also introduces a new probabilistic bound (called "Chernoff-Wald bound") for bounding the expectation of the maximum of a collection of sums of random variables, when each sum contains a random number of terms. The bound is used to analyze the randomized rounding scheme that underlies the algorithms.

Connected through hyperedge HE-1 (induced by source paper "submodular function maximization in parallel via the multilinear relaxation"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular optimization in the mapreduce model"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "streaming algorithms for submodular function maximization"
    - "a new framework for distributed submodular maximization"

Connected through hyperedge HE-2 (induced by source paper "nearly linear work algorithms for mixed packing covering and facility location linear programs"):
  with members:
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "using optimization to solve positive lps faster in parallel"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"
    - "sequential and parallel algorithms for mixed packing and covering"

Connected through hyperedge HE-3 (induced by source paper "a nearly linear time ptas for explicit fractional packing and covering linear programs"):
  with members:
    - "sublinear optimization for machine learning"
    - "sequential and parallel algorithms for mixed packing and covering"
    - "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms"

Connected through hyperedge HE-4 (induced by source paper "incremental medians via online bidding"):
  with members:
    - "the reverse greedy algorithm for the metric k median problem"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-5 (induced by source paper "sequential and parallel algorithms for mixed packing and covering"):
  with members:
    - "randomized rounding without solving the linear program"
    - "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms"

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)