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: "theoretical analysis of the k means algorithm a survey"
  Abstract: The $k$-means algorithm is one of the most widely used clustering heuristics. Despite its simplicity, analyzing its running time and quality of approximation is surprisingly difficult and can lead to deep insights that can be used to improve the algorithm. In this paper we survey the recent results in this direction as well as several extension of the basic $k$-means method.

Connected through hyperedge HE-1 (induced by source paper "distance geometry and data science"):
  with members:
    - "global optimality conditions for deep neural networks"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "recovering the optimal solution by dual random projection"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "training free artificial neural networks"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-3 (induced by source paper "local search yields a ptas for k means in doubling metrics"):
  with members:
    - "the power of local search for clustering"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "on variants of k means clustering"
    - "the hardness of approximation of euclidean k means"
    - "the unreasonable success of local search geometric optimization"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "clustering with spectral norm and the k means 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)