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: "greedy minimization of weakly supermodular set functions"
  Abstract: This paper defines weak-$\alpha$-supermodularity for set functions. Many optimization objectives in machine learning and data mining seek to minimize such functions under cardinality constrains. We prove that such problems benefit from a greedy extension phase. Explicitly, let $S^*$ be the optimal set of cardinality $k$ that minimizes $f$ and let $S_0$ be an initial solution such that $f(S_0)/f(S^*) \le \rho$. Then, a greedy extension $S \supset S_0$ of size $|S| \le |S_0| + \lceil \alpha k \ln(\rho/\varepsilon) \rceil$ yields $f(S)/f(S^*) \le 1+\varepsilon$. As example usages of this framework we give new bicriteria results for $k$-means, sparse regression, and columns subset selection.

Connected through hyperedge HE-1 (induced by source paper "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"):
  with members:
    - "beyond submodular maximization"
    - "fast parallel algorithms for feature selection"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "greed is still good maximizing monotone submodular supermodular functions"
    - "fast greedy algorithms for dictionary selection with generalized sparsity constraints"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-2 (induced by source paper "fast greedy algorithms for dictionary selection with generalized sparsity constraints"):
  with members:
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-3 (induced by source paper "adaptive feature selection computationally efficient online sparse linear regression under rip"):
  with members:
    - "online sparse linear regression"
    - "learning without concentration"
    - "variable selection is hard"
    - "linear regression with limited observation"
    - "efficient learning with partially observed attributes"

Connected through hyperedge HE-4 (induced by source paper "greedy column subset selection new bounds and distributed algorithms"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "lazier than lazy greedy"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "a fast greedy algorithm for generalized column subset selection"

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)