human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi dimensional balanced graph partitioning via projected gradient descent"):
  Abstract: Motivated by performance optimization of large-scale graph processing systems that distribute the graph across multiple machines, we consider the balanced graph partitioning problem. Compared to the previous work, we study the multi-dimensional variant when balance according to multiple weight functions is required. As we demonstrate by experimental evaluation, such multi-dimensional balance is important for achieving performance improvements for typical distributed graph processing workloads. We propose a new scalable technique for the multidimensional balanced graph partitioning problem. The method is based on applying randomized projected gradient descent to a non-convex continuous relaxation of the objective. We show how to implement the new algorithm efficiently in both theory and practice utilizing various approaches for projection. Experiments with large-scale social networks containing up to hundreds of billions of edges indicate that our algorithm has superior performance compared with the state-of-the-art approaches.

Includes members:
  Member "vebo a vertex and edge balanced ordering heuristic to load balance parallel graph processing":
    also connected through other hyperedges:
      (none)
  Member "matrix completion has no spurious local minimum":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "compressing graphs and indexes with recursive graph bisection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "techniques for inverted index compression")
  Member "efficient approaches for escaping higher order saddle points in non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "necpd an online tensor decomposition with optimal stochastic gradient descent")
  Member "distributed balanced partitioning via linear embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "randomized experimental design via geographic clustering")
  Member "escaping from saddle points online stochastic gradient for tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "spinner scalable graph partitioning in the cloud":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed edge partitioning for trillion edge graphs")
  Member "partitioning complex networks via size constrained clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable graph algorithms")

Question: Please tell me which class the source paper 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)