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: "partitioning complex networks via size constrained clustering"
  Abstract: The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm reduces the graph size by iteratively contracting nodes and edges until the graph is small enough to be partitioned by some other algorithm. A partition of the input graph is then constructed by successively transferring the solution to the next finer graph and applying a local search algorithm to improve the current solution. #R##N#In this paper, we describe a novel approach to partition graphs effectively especially if the networks have a highly irregular structure. More precisely, our algorithm provides graph coarsening by iteratively contracting size-constrained clusterings that are computed using a label propagation algorithm. The same algorithm that provides the size-constrained clusterings can also be used during uncoarsening as a fast and simple local search algorithm. #R##N#Depending on the algorithm's configuration, we are able to compute partitions of very high quality outperforming all competitors, or partitions that are comparable to the best competitor in terms of quality, hMetis, while being nearly an order of magnitude faster on average. The fastest configuration partitions the largest graph available to us with 3.3 billion edges using a single machine in about ten minutes while cutting less than half of the edges than the fastest competitor, kMetis.

Connected through hyperedge HE-1 (induced by source paper "scalable graph algorithms"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "memetic graph clustering"
    - "practical minimum cut algorithms"
    - "scalable edge partitioning"
    - "shared memory exact minimum cuts"
    - "scalable kernelization for maximum independent sets"
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"

Connected through hyperedge HE-2 (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"
    - "distributed balanced partitioning via linear embedding"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "spinner scalable graph partitioning in the cloud"
    - "nonuniform graph partitioning with unrelated weights"

Connected through hyperedge HE-3 (induced by source paper "faster support vector machines"):
  with members:
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "engineering multilevel support vector machines"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "parallel graph partitioning for complex networks"
    - "recent advances in graph partitioning"
    - "parallel graph decompositions using random shifts"
    - "a divide and conquer solver for kernel support vector machines"

Connected through hyperedge HE-4 (induced by source paper "network flow based refinement for multilevel hypergraph partitioning"):
  with members:
    - "aggregative coarsening for multilevel hypergraph partitioning"
    - "hype massive hypergraph partitioning with neighborhood expansion"
    - "memetic multilevel hypergraph partitioning"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "graph bisection with pareto optimization"
    - "engineering multilevel graph partitioning algorithms"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-5 (induced by source paper "high quality shared memory graph partitioning"):
  with members:
    - "in place parallel super scalar samplesort ips 4 o"
    - "engineering parallel algorithms for community detection in massive networks"
    - "concurrent hash tables fast and general"
    - "parallel graph partitioning for complex networks"
    - "recent advances in graph partitioning"
    - "the power of simple tabulation hashing"
    - "n level graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-6 (induced by source paper "an empirical comparison of big graph frameworks in the context of network analysis"):
  with members:
    - "networkit a tool suite for large scale complex network analysis"
    - "distributed graphlab a framework for machine learning in the cloud"

Connected through hyperedge HE-7 (induced by source paper "drawing large graphs by multilevel maxent stress optimization"):
  with members:
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "a distributed multilevel force directed algorithm"
    - "a sparse stress model"
    - "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"
    - "coast a convex optimization approach to stress based embedding"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-8 (induced by source paper "distributed graph layout for scalable small world network analysis"):
  with members:
    - "xdgp a dynamic graph processing system with adaptive partitioning"
    - "defining and evaluating network communities based on ground truth"
    - "parallel breadth first search on distributed memory systems"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "graphlab a new framework for parallel machine learning"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)