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: "local search based approximation algorithms for mobile facility location problems"
  Abstract: We consider the {\em mobile facility location} (\mfl) problem. We are given a set of facilities and clients located in a common metric space. The goal is to move each facility from its initial location to a destination and assign each client to the destination of some facility so as to minimize the sum of the movement-costs of the facilities and the client-assignment costs. This abstracts facility-location settings where one has the flexibility of moving facilities from their current locations to other destinations so as to serve clients more efficiently by reducing their assignment costs. #R##N#We give the first {\em local-search based} approximation algorithm for this problem and achieve the best-known approximation guarantee. Our main result is $(3+\epsilon)$-approximation for this problem for any constant $\epsilon>0$ using local search. The previous best guarantee was an 8-approximation algorithm based on LP-rounding. Our guarantee {\em matches} the best-known approximation guarantee for the $k$-median problem. Since there is an approximation-preserving reduction from the $k$-median problem to \mfl, any improvement of our result would imply an analogous improvement for the $k$-median problem. Furthermore, {\em our analysis is tight} (up to $o(1)$ factors) since the tight example for the local-search based 3-approximation algorithm for $k$-median can be easily adapted to show that our local-search algorithm has a tight approximation ratio of 3. One of the chief novelties of the analysis is that in order to generate a suitable collection of local-search moves whose resulting inequalities yield the desired bound on the cost of a local-optimum, we define a tree-like structure that (loosely speaking) functions as a "recursion tree", using which we spawn off local-search moves by exploring this tree to a constant depth.

Connected through hyperedge HE-1 (induced by source paper "sets clustering"):
  with members:
    - "introduction to coresets accurate coresets"
    - "tight sensitivity bounds for smaller coresets"
    - "k means clustering of lines for big data"
    - "on coresets for logistic regression"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "k means for streaming and distributed big sparse data"
    - "improved and simplified inapproximability for k means"

Connected through hyperedge HE-2 (induced by source paper "constant factor approximation algorithms for parity constrained facility location problems"):
  with members:
    - "a tree structure for dynamic facility location"
    - "on facility location with general lower bounds"
    - "online facility location with deletions"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "facility location in evolving metrics"
    - "lp based algorithms for capacitated facility location"
    - "eight fifth approximation for the path tsp"
    - "fast clustering with lower bounds no customer too far no shop too small"

Connected through hyperedge HE-3 (induced by source paper "on re balancing self interested agents in ride sourcing transportation networks"):
  with members:
    - "stochastic model predictive control for autonomous mobility on demand"
    - "voronoi game on graphs"

Connected through hyperedge HE-4 (induced by source paper "a local search algorithm for steiner forest"):
  with members:
    - "local search yields a ptas for k means in doubling metrics"
    - "simple ptas s for families of graphs excluding a minor"
    - "online steiner tree with deletions"

Connected through hyperedge HE-5 (induced by source paper "serving online requests with mobile servers"):
  with members:
    - "a polylogarithmic competitive algorithm for the k server problem"
    - "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-6 (induced by source paper "serving online demands with movable centers"):
  with members:
    - "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-7 (induced by source paper "approximation algorithms for movement repairmen"):
  with members:
    - "a ptas for tsp with neighborhoods among fat regions in the plane"
    - "minimizing movement fixed parameter tractability"
    - "facility location with client latencies linear programming based techniques for minimum latency problems"
    - "algorithms for rapidly dispersing robot swarms in unknown environments"
    - "approximation algorithms for tsp with neighborhoods in the plane"

Connected through hyperedge HE-8 (induced by source paper "improved approximation algorithms for matroid and knapsack median problems and applications"):
  with members:
    - "facility location with client latencies linear programming based techniques for minimum latency problems"

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)