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: "approximation algorithms for clustering problems with lower bounds and outliers"
  Abstract: We consider clustering problems with {\em non-uniform lower bounds and outliers}, and obtain the {\em first approximation guarantees} for these problems. We have a set $\F$ of facilities with lower bounds $\{L_i\}_{i\in\F}$ and a set $\D$ of clients located in a common metric space $\{c(i,j)\}_{i,j\in\F\cup\D}$, and bounds $k$, $m$. A feasible solution is a pair $\bigl(S\sse\F,\sigma:\D\mapsto S\cup\{\mathsf{out}\}\bigr)$, where $\sigma$ specifies the client assignments, such that $|S|\leq k$, $|\sigma^{-1}(i)|\geq L_i$ for all $i\in S$, and $|\sigma^{-1}(\mathsf{out})|\leq m$. In the {\em lower-bounded min-sum-of-radii with outliers} (\lbksro) problem, the objective is to minimize $\sum_{i\in S}\max_{j\in\sigma^{-1}(i)}c(i,j)$, and in the {\em lower-bounded $k$-supplier with outliers} (\lbkso) problem, the objective is to minimize $\max_{i\in S}\max_{j\in\sigma^{-1}(i)}c(i,j)$. #R##N#We obtain an approximation factor of $12.365$ for \lbksro, which improves to $3.83$ for the non-outlier version (i.e., $m=0$). These also constitute the {\em first} approximation bounds for the min-sum-of-radii objective when we consider lower bounds and outliers {\em separately}. We apply the primal-dual method to the relaxation where we Lagrangify the $|S|\leq k$ constraint. The chief technical contribution and novelty of our algorithm is that, departing from the standard paradigm used for such constrained problems, we obtain an $O(1)$-approximation {\em despite the fact that we do not obtain a Lagrangian-multiplier-preserving algorithm for the Lagrangian relaxation}. We believe that our ideas have {broader applicability to other clustering problems with outliers as well.} #R##N#We obtain approximation factors of $5$ and $3$ respectively for \lbkso and its non-outlier version. These are the {\em first} approximation results for $k$-supplier with {\em non-uniform} lower bounds.

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 "faster balanced clusterings in high dimension"):
  with members:
    - "privacy preserving clustering with constraints"
    - "balanced k center clustering when k is a constant"
    - "capacitated center problems with two sided bounds and outliers"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "distributed balanced partitioning via linear embedding"
    - "data driven resource allocation for distributed learning"
    - "faster algorithms for the constrained k means problem"

Connected through hyperedge HE-3 (induced by source paper "robust communication optimal distributed clustering algorithms"):
  with members:
    - "perturbation resilient clustering for k center and related problems via lp relaxations"
    - "distributed partial clustering"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "communication optimal distributed clustering"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "data driven resource allocation for distributed learning"
    - "k center clustering under perturbation resilience"
    - "a bi criteria approximation algorithm for k means"

Connected through hyperedge HE-4 (induced by source paper "approximation schemes for clustering with outliers"):
  with members:
    - "a fast approximation scheme for low dimensional k means"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "the non uniform k center problem"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "on variants of k means clustering"
    - "improved and simplified inapproximability for k means"

Connected through hyperedge HE-5 (induced by source paper "capacitated center problems with two sided bounds and outliers"):
  with members:
    - "improved approximation algorithms for capacitated fault tolerant k center"
    - "data driven resource allocation for distributed learning"
    - "constant factor approximation for capacitated k center with outliers"
    - "matroid and knapsack center problems"
    - "fast clustering with lower bounds no customer too far no shop too small"
    - "approximating k median via pseudo approximation"
    - "lp rounding for k centers with non uniform hard capacities"
    - "clustering with diversity"

Connected through hyperedge HE-6 (induced by source paper "balanced k center clustering when k is a constant"):
  with members:
    - "capacitated center problems with two sided bounds and outliers"
    - "distributed balanced partitioning via linear embedding"
    - "data driven resource allocation for distributed learning"
    - "constant factor approximation for capacitated k center with outliers"
    - "fast clustering with lower bounds no customer too far no shop too small"
    - "lp rounding for k centers with non uniform hard capacities"

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)