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: "improved approximation algorithms for matroid and knapsack median problems and applications"
  Abstract: We consider the {\em matroid median} problem \cite{KrishnaswamyKNSS11}, wherein we are given a set of facilities with opening costs and a matroid on the facility-set, and clients with demands and connection costs, and we seek to open an independent set of facilities and assign clients to open facilities so as to minimize the sum of the facility-opening and client-connection costs. We give a simple 8-approximation algorithm for this problem based on LP-rounding, which improves upon the 16-approximation in \cite{KrishnaswamyKNSS11}. Our techniques illustrate that much of the work involved in the rounding algorithm of in \cite{KrishnaswamyKNSS11} can be avoided by first converting the LP solution to a half-integral solution, which can then be rounded to an integer solution using a simple uncapacitated-facility-location (UFL) style clustering step. We illustrate the power of these ideas by deriving: (a) a 24-approximation algorithm for matroid median with penalties, which is a vast improvement over the 360-approximation obtained in \cite{KrishnaswamyKNSS11}; and (b) an 8-approximation for the {\em two-matroid median} problem, a generalization of matroid median that we introduce involving two matroids. We show that a variety of seemingly disparate facility-location problems considered in the literature---data placement problem, mobile facility location, $k$-median forest, metric uniform minimum-latency UFL---in fact reduce to the matroid median or two-matroid median problems, and thus obtain improved approximation guarantees for all these problems. Our techniques also yield an improvement for the knapsack median problem.

Connected through hyperedge HE-1 (induced by source paper "fair algorithms for clustering"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "constant factor fpt approximation for capacitated k median"
    - "a unified framework for clustering constrained data without locality property"
    - "fair clustering through fairlets"
    - "generalized center problems with outliers"

Connected through hyperedge HE-2 (induced by source paper "fixed parameter algorithms for maximum profit facility location under matroid constraints"):
  with members:
    - "inferring the strength of social ties a community driven approach"
    - "on the relation of strong triadic closure and cluster deletion"
    - "parameterized aspects of strong subgraph closure"
    - "deterministic truncation of linear matroids"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "parameterized exact and approximation algorithms for maximum k set cover and related satisfiability problems"

Connected through hyperedge HE-3 (induced by source paper "constant approximation for k median and k means with outliers via iterative rounding"):
  with members:
    - "approximation schemes for clustering with outliers"
    - "the power of local search for clustering"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "improved and simplified inapproximability for k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-4 (induced by source paper "a lottery model for center type problems with outliers"):
  with members:
    - "symmetric randomized dependent rounding"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "the non uniform k center problem"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "matroid and knapsack center problems"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-5 (induced by source paper "constant factor approximation algorithms for uniform hard capacitated facility location problems natural lp is not too bad"):
  with members:
    - "constant approximation for capacitated k median with 1 epsilon capacity violation"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "an improved approximation algorithm for the hard uniform capacitated k median problem"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "lp based algorithms for capacitated facility location"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "bi factor approximation algorithms for hard capacitated k median problems"

Connected through hyperedge HE-6 (induced by source paper "matroid and knapsack center problems"):
  with members:
    - "lp rounding for k centers with non uniform hard capacities"
    - "matroidal degree bounded minimum spanning trees"
    - "clustering with diversity"

Connected through hyperedge HE-7 (induced by source paper "local search based approximation algorithms for mobile facility location problems"):
  with members:
    - "simpler analyses of local search algorithms for facility location"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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)