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: "facility location with client latencies linear programming based techniques for minimum latency problems"
  Abstract: We introduce a problem that is a common generalization of the uncapacitated facility location and minimum latency (ML) problems, where facilities need to be opened to serve clients and also need to be sequentially activated before they can provide service. Formally, we are given a set \F of n facilities with facility-opening costs {f_i}, a set of m clients, and connection costs {c_{ij}} specifying the cost of assigning a client j to a facility i, a root node r denoting the depot, and a time metric d on \F\cup{r}. Our goal is to open a subset F of facilities, find a path P starting at r and spanning F to activate the open facilities, and connect each client j to a facility \phi(j)\in F, so as to minimize \sum_{i\in F}f_i +\sum_{clients j}(c_{\phi(j),j}+t_j), where t_j is the time taken to reach \phi(j) along path P. We call this the minimum latency uncapacitated facility location (MLUFL) problem. #R##N#Our main result is an O(\log n\max{\log n,\log m})-approximation for MLUFL. We also show that any improvement in this approximation guarantee, implies an improvement in the (current-best) approximation factor for group Steiner tree. We obtain constant approximations for two natural special cases of the problem: (a) related MLUFL (metric connection costs that are a scalar multiple of the time metric); (b) metric uniform MLUFL (metric connection costs, unform time-metric). Our LP-based methods are versatile and easily adapted to yield approximation guarantees for MLUFL in various more general settings, such as (i) when the latency-cost of a client is a function of the delay faced by the facility to which it is connected; and (ii) the k-route version, where k vehicles are routed in parallel to activate the open facilities. Our LP-based understanding of MLUFL also offers some LP-based insights into ML, which we believe is a promising direction for obtaining improvements for ML.

Connected through hyperedge HE-1 (induced by source paper "compact provably good lps for orienteering and regret bounded vehicle routing"):
  with members:
    - "an improved integrality gap for asymmetric tsp paths"
    - "approximation algorithms for regret bounded vehicle routing and applications to distance constrained vehicle routing"

Connected through hyperedge HE-2 (induced by source paper "linear programming based approximation algorithms for multi vehicle minimum latency problems"):
  with members:
    - "polynomial time approximation schemes for the traveling repairman and other minimum latency problems"

Connected through hyperedge HE-3 (induced by source paper "approximation algorithms for movement repairmen"):
  with members:
    - "a ptas for tsp with neighborhoods among fat regions in the plane"
    - "local search based approximation algorithms for mobile facility location problems"
    - "minimizing movement fixed parameter tractability"
    - "algorithms for rapidly dispersing robot swarms in unknown environments"
    - "approximation algorithms for tsp with neighborhoods in the plane"

Connected through hyperedge HE-4 (induced by source paper "approximation algorithms for regret bounded vehicle routing and applications to distance constrained vehicle routing"):
  with members:
    - "approximating bin packing within o log opt log log opt bins"
    - "multiple traveling salesmen in asymmetric metrics"
    - "santa claus schedules jobs on unrelated machines"
    - "asymmetric traveling salesman path and directed latency problems"

Connected through hyperedge HE-5 (induced by source paper "improved approximation algorithms for matroid and knapsack median problems and applications"):
  with members:
    - "local search based approximation algorithms for mobile facility location problems"

Connected through hyperedge HE-6 (induced by source paper "multiple traveling salesmen in asymmetric metrics"):
  with members:
    - "eight fifth approximation for the path tsp"
    - "an improved integrality gap for asymmetric tsp paths"
    - "new inapproximability bounds for tsp"
    - "improving christofides algorithm for the s t path tsp"
    - "asymmetric traveling salesman path and directed latency problems"

Connected through hyperedge HE-7 (induced by source paper "minimum latency submodular cover"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "a note on the generalized min sum set cover problem"
    - "ranking with submodular valuations"

Connected through hyperedge HE-8 (induced by source paper "asymmetric traveling salesman path and directed latency problems"):
  with members:
    - "improving christofides algorithm for the s t path tsp"

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)