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: "small space stream summary for matroid center"
  Abstract: In the matroid center problem, which generalizes the $k$-center problem, we need to pick a set of centers that is an independent set of a matroid with rank $r$. We study this problem in streaming, where elements of the ground set arrive in the stream. We first show that any randomized one-pass streaming algorithm that computes a better than $\Delta$-approximation for partition-matroid center must use $\Omega(r^2)$ bits of space, where $\Delta$ is the aspect ratio of the metric and can be arbitrarily large. This shows a quadratic separation between matroid center and $k$-center, for which the Doubling algorithm gives an $8$-approximation using $O(k)$-space and one pass. To complement this, we give a one-pass algorithm for matroid center that stores at most $O(r^2\log(1/\varepsilon)/\varepsilon)$ points (viz., stream summary) among which a $(7+\varepsilon)$-approximate solution exists, which can be found by brute force, or a $(17+\varepsilon)$-approximation can be found with an efficient algorithm. If we are allowed a second pass, we can compute a $(3+\varepsilon)$-approximation efficiently. #R##N#We also consider the problem of matroid center with $z$ outliers and give a one-pass algorithm that outputs a set of $O((r^2+rz)\log(1/\varepsilon)/\varepsilon)$ points that contains a $(15+\varepsilon)$-approximate solution. Our techniques extend to knapsack center and knapsack center with outliers in a straightforward way, and we get algorithms that use space linear in the size of a largest feasible set (as opposed to quadratic space for matroid center).

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"
    - "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"
    - "matroid and knapsack center problems"
    - "space round tradeoffs for mapreduce computations"

Connected through hyperedge HE-2 (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"
    - "deep learning face attributes in the wild"
    - "matroid and knapsack center problems"

Connected through hyperedge HE-3 (induced by source paper "fully dynamic k center clustering in doubling metrics"):
  with members:
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "a tree structure for dynamic facility location"
    - "fully dynamic hierarchical diameter k clustering and k center"
    - "expander decomposition and pruning faster stronger and simpler"
    - "dynamic clustering to minimize the sum of radii"
    - "the new data and new challenges in multimedia research"
    - "fast construction of nets in low dimensional metrics and their applications"

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)