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: "interpolating between k median and k center approximation algorithms for ordered k median"
  Abstract: We consider a generalization of $k$-median and $k$-center, called the {\em ordered $k$-median} problem. In this problem, we are given a metric space $(\mathcal{D},\{c_{ij}\})$ with $n=|\mathcal{D}|$ points, and a non-increasing weight vector $w\in\mathbb{R}_+^n$, and the goal is to open $k$ centers and assign each point each point $j\in\mathcal{D}$ to a center so as to minimize $w_1\cdot\text{(largest assignment cost)}+w_2\cdot\text{(second-largest assignment cost)}+\ldots+w_n\cdot\text{($n$-th largest assignment cost)}$. We give an $(18+\epsilon)$-approximation algorithm for this problem. Our algorithms utilize Lagrangian relaxation and the primal-dual schema, combined with an enumeration procedure of Aouad and Segev. For the special case of $\{0,1\}$-weights, which models the problem of minimizing the $\ell$ largest assignment costs that is interesting in and of by itself, we provide a novel reduction to the (standard) $k$-median problem showing that LP-relative guarantees for $k$-median translate to guarantees for the ordered $k$-median problem; this yields a nice and clean $(8.5+\epsilon)$-approximation algorithm for $\{0,1\}$ weights.

Connected through hyperedge HE-1 (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"
    - "constant factor approximation for ordered k median"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-2 (induced by source paper "simpler and better algorithms for minimum norm load balancing"):
  with members:
    - "approximation algorithms for minimum norm and ordered optimization problems"
    - "constant factor approximation 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-3 (induced by source paper "reverse greedy is bad for k center"):
  with members:
    - "approximation algorithms for minimum norm and ordered optimization problems"
    - "constant factor approximation 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"

Connected through hyperedge HE-4 (induced by source paper "constant factor approximation for ordered k median"):
  with members:
    - "symmetric randomized dependent rounding"
    - "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"
    - "approximating k median via pseudo approximation"
    - "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)