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: "distributed balanced partitioning via linear embedding"
  Abstract: Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, e.g., in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together. In other cases, links between different parts may show up in the running time and/or network communications cost. We study a distributed balanced partitioning problem where the goal is to partition the vertices of a given graph into k pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, e.g., via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, e.g., a label propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: e.g., compared to the label propagation algorithm, we report an improvement of 15-25% in the cut value. We also observe that our algorithms allow for scalable distributed implementation for any number of partitions. Finally, we apply our techniques for the Google Maps Driving Directions to minimize the number of multi-shard queries with the goal of saving in CPU usage. During live experiments, we observe an ~40% drop in the number of multi-shard queries when comparing our method with a standard geography-based method.

Connected through hyperedge HE-1 (induced by source paper "multi dimensional balanced graph partitioning via projected gradient descent"):
  with members:
    - "vebo a vertex and edge balanced ordering heuristic to load balance parallel graph processing"
    - "matrix completion has no spurious local minimum"
    - "compressing graphs and indexes with recursive graph bisection"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "spinner scalable graph partitioning in the cloud"
    - "partitioning complex networks via size constrained clustering"
    - "nonuniform graph partitioning with unrelated weights"

Connected through hyperedge HE-2 (induced by source paper "randomized experimental design via geographic clustering"):
  with members:
    - "graph cluster randomization network exposure to multiple universes"
    - "finding connected components on map reduce in logarithmic rounds"

Connected through hyperedge HE-3 (induced by source paper "faster balanced clusterings in high dimension"):
  with members:
    - "privacy preserving clustering with constraints"
    - "balanced k center clustering when k is a constant"
    - "capacitated center problems with two sided bounds and outliers"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "data driven resource allocation for distributed learning"
    - "faster algorithms for the constrained k means problem"

Connected through hyperedge HE-4 (induced by source paper "onion curve a space filling curve with near optimal clustering"):
  with members:
    - "load balancing using hilbert space filling curves for parallel reservoir simulations"
    - "recursive tilings and space filling curves with little fragmentation"

Connected through hyperedge HE-5 (induced by source paper "balanced k center clustering when k is a constant"):
  with members:
    - "capacitated center problems with two sided bounds and outliers"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "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"
    - "lp rounding for k centers with non uniform hard capacities"

Connected through hyperedge HE-6 (induced by source paper "geocuts geographic clustering using travel statistics"):
  with members:
    - "graph cluster randomization network exposure to multiple universes"
    - "finding connected components on map reduce in logarithmic rounds"

Connected through hyperedge HE-7 (induced by source paper "spinner scalable graph partitioning in the cloud"):
  with members:
    - "parallel graph partitioning for complex networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "defining and evaluating network communities based on ground truth"

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.DC(Distributed, Parallel, and Cluster Computing)