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: "constant factor approximation for ordered k median"
  Abstract: We study the Ordered k-Median problem, in which the solution is evaluated by first sorting the client connection costs and then multiplying them with a predefined non-increasing weight vector (higher connection costs are taken with larger weights). The problem unifies many fundamental clustering and location problems such as k-Median and k-Center. This generality, however, renders the problem intriguing from the algorithmic perspective. Recently, Aouad and Segev proposed a sophisticated local-search based O(log n) approximation algorithm for Ordered k-Median, extending the result by Tamir (2001) for the case of a rectangular weight vector, also known as k-Facility p-Centrum. The existence of a constant-factor approximation algorithm remained open, even for the special case with a rectangular weight vector. #R##N#Our main result is an LP-rounding constant-factor approximation algorithm for the (general) Ordered k-Median problem. #R##N#We first provide a new analysis of the rounding process by Charikar and Li (2012) for k-Median, when applied to a fractional solution obtained from solving an LP with a carefully modified objective function, results in an elegant 15-approximation for the rectangular case. In our analysis, the connection cost of a single client is partly charged to a deterministic budget related to a combinatorial bound based on guessing, and partly to a budget whose expected value is bounded with respect to the fractional LP-solution. Next we analyze objective-oblivious clustering that allows to handle multiple rectangles in the weight vector. Finally, with a more involved argument, we show that the clever distance bucketing by Aouad and Segev can be combined with the objective-oblivious version of our LP-rounding for the rectangular case, and that it results in a true, polynomial time, constant-factor approximation algorithm.

Connected through hyperedge HE-1 (induced by source paper "reconciliation k median clustering with non polarized representatives"):
  with members:
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "reducing controversy by connecting opposing views"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-2 (induced by source paper "coresets for ordered weighted clustering"):
  with members:
    - "privacy preserving clustering with constraints"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fair clustering through fairlets"
    - "approximation algorithms for minimum norm and ordered optimization problems"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "interpolating between k median and k center approximation algorithms for ordered k median"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-3 (induced by source paper "simpler and better algorithms for minimum norm load balancing"):
  with members:
    - "approximation algorithms for minimum norm and ordered optimization problems"
    - "interpolating between k median and k center approximation algorithms for ordered k median"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "on the configuration lp of the restricted assignment problem"

Connected through hyperedge HE-4 (induced by source paper "approximation algorithms for minimum norm and ordered optimization problems"):
  with members:
    - "approximate near neighbors for general symmetric norms"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "on the configuration lp of the restricted assignment problem"
    - "streaming symmetric norms via measure concentration"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "santa claus schedules jobs on unrelated machines"
    - "simpler analyses of local search algorithms for facility location"

Connected through hyperedge HE-5 (induced by source paper "reverse greedy is bad for k center"):
  with members:
    - "approximation algorithms for minimum norm and ordered optimization problems"
    - "interpolating between k median and k center approximation algorithms for ordered k median"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "clustering under perturbation resilience"
    - "center based clustering under perturbation stability"
    - "the reverse greedy algorithm for the metric k median problem"

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)