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: "engineering a scalable high quality graph partitioner"
  Abstract: We describe an approach to parallel graph partitioning that scales to hundreds of processors and produces a high solution quality. For example, for many instances from Walshaw's benchmark collection we improve the best known partitioning. We use the well known framework of multi-level graph partitioning. All components are implemented by scalable parallel algorithms. Quality improvements compared to previous systems are due to better prioritization of edges to be contracted, better approximation algorithms for identifying matchings, better local search heuristics, and perhaps most notably, a parallelization of the FM local search algorithm that works more locally than previous approaches.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (induced by source paper "a comparison of partitioning strategies in ac optimal power flow"):
  with members:
    - "k way hypergraph partitioning via n level recursive bisection"
    - "recent advances in graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-3 (induced by source paper "weighted laplacian and its theoretical applications"):
  with members:
    - "sampling and multilevel coarsening algorithms for fast matrix approximations"
    - "community detection and graph partitioning"
    - "advanced coarsening schemes for graph partitioning"

Connected through hyperedge HE-4 (induced by source paper "balanced k means for parallel geometric partitioning"):
  with members:
    - "communication free massively distributed graph generation"
    - "lightweight mpi communicators with applications to perfectly balanced quicksort"
    - "partitioning trillion edge graphs in minutes"
    - "parallel graph partitioning for complex networks"
    - "recent advances in graph partitioning"

Connected through hyperedge HE-5 (induced by source paper "scalable kernelization for maximum independent sets"):
  with members:
    - "exact algorithms for maximum independent set"
    - "accelerating local search for the maximum independent set problem"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "on the power of simple reductions for the maximum independent set problem"
    - "finding near optimal independent sets at scale"
    - "evaluation of labeling strategies for rotating maps"
    - "parallel graph partitioning for complex networks"
    - "kernelization using structural parameters on sparse graph classes"

Connected through hyperedge HE-6 (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"
    - "partitioning complex networks via size constrained clustering"
    - "recent advances in graph partitioning"
    - "the power of simple tabulation hashing"
    - "n level graph partitioning"

Connected through hyperedge HE-7 (induced by source paper "communication free massively distributed graph generation"):
  with members:
    - "efficient parallel random sampling vectorized cache efficient and online"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "scalable generation of scale free graphs"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "geometric inhomogeneous random graphs"

Connected through hyperedge HE-8 (induced by source paper "evolutionary acyclic graph partitioning"):
  with members:
    - "graph partitioning with acyclicity constraints"
    - "recent advances in graph partitioning"
    - "distributed evolutionary graph partitioning"
    - "asymptotically optimal randomized rumor spreading"
    - "engineering multilevel graph partitioning algorithms"

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)