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: "matroid and knapsack center problems"
  Abstract: In the classic $k$-center problem, we are given a metric graph, and the objective is to open $k$ nodes as centers such that the maximum distance from any vertex to its closest center is minimized. In this paper, we consider two important generalizations of $k$-center, the matroid center problem and the knapsack center problem. Both problems are motivated by recent content distribution network applications. Our contributions can be summarized as follows: #R##N#1. We consider the matroid center problem in which the centers are required to form an independent set of a given matroid. We show this problem is NP-hard even on a line. We present a 3-approximation algorithm for the problem on general metrics. We also consider the outlier version of the problem where a given number of vertices can be excluded as the outliers from the solution. We present a 7-approximation for the outlier version. #R##N#2. We consider the (multi-)knapsack center problem in which the centers are required to satisfy one (or more) knapsack constraint(s). It is known that the knapsack center problem with a single knapsack constraint admits a 3-approximation. However, when there are at least two knapsack constraints, we show this problem is not approximable at all. To complement the hardness result, we present a polynomial time algorithm that gives a 3-approximate solution such that one knapsack constraint is satisfied and the others may be violated by at most a factor of $1+\epsilon$. We also obtain a 3-approximation for the outlier version that may violate the knapsack constraint by $1+\epsilon$.

Connected through hyperedge HE-1 (induced by source paper "coreset based strategies for robust center type problems"):
  with members:
    - "a general coreset based approach to diversity maximization under matroid constraints"
    - "small space stream summary for matroid center"
    - "generalized center problems with outliers"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "a lottery model for center type problems with outliers"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-2 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints"):
  with members:
    - "on a generalization of iterated and randomized rounding"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximation friendly discrepancy rounding"
    - "approximating k median via pseudo approximation"
    - "fault tolerant facility location a randomized dependent lp rounding algorithm"

Connected through hyperedge HE-3 (induced by source paper "how to solve fair k center in massive data models"):
  with members:
    - "a constant approximation for colorful k center"
    - "clustering without over representation"
    - "fair clustering through fairlets"
    - "small space stream summary for matroid center"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "a constant approximation for colorful k center"):
  with members:
    - "on the partition set cover problem"
    - "approximation schemes for clustering with outliers"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-5 (induced by source paper "generalized center problems with outliers"):
  with members:
    - "a lottery model for center type problems with outliers"
    - "the heterogeneous capacitated k center problem"
    - "the non uniform k center problem"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "lp based algorithms for capacitated facility location"
    - "approximating capacitated k median with 1 epsilon k open facilities"

Connected through hyperedge HE-6 (induced by source paper "capacitated center problems with two sided bounds and outliers"):
  with members:
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "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"
    - "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-7 (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"
    - "improved approximation algorithms for matroid and knapsack median problems and applications"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-8 (induced by source paper "fairness in resource allocation and slowed down dependent rounding"):
  with members:
    - "approximating k median via pseudo approximation"
    - "fault tolerant facility location a randomized dependent lp rounding algorithm"

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)