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: "efficient parallel random sampling vectorized cache efficient and online"
  Abstract: We consider the problem of sampling n numbers from the range l 1,… ,Nr without replacement on modern architectures. The main result is a simple divide-and-conquer scheme that makes sequential algorithms more cache efficient and leads to a parallel algorithm running in expected time O(n/p+log p) on p processors, i.e., scales to massively parallel machines even for moderate values of n. The amount of communication between the processors is very small (at most O(log p)) and independent of the sample size. We also discuss modifications needed for load balancing, online sampling, sampling with replacement, Bernoulli sampling, and vectorization on SIMD units or GPUs.

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"
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-2 (induced by source paper "communication efficient weighted reservoir sampling from fully distributed data streams"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "parallel weighted random sampling"
    - "parallel streaming random sampling"
    - "weighted sampling without replacement from data streams"
    - "fast parallel operations on search trees"

Connected through hyperedge HE-3 (induced by source paper "load balancing for parallel delaunay triangulations"):
  with members:
    - "high quality shared memory graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-4 (induced by source paper "parallel weighted random sampling"):
  with members:
    - "thrill high performance algorithmic distributed batch data processing with c"
    - "improved parallel construction of wavelet trees and rank select structures"

Connected through hyperedge HE-5 (induced by source paper "parallel streaming random sampling"):
  with members:
    - "parallel triangle counting in massive streaming graphs"

Connected through hyperedge HE-6 (induced by source paper "communication free massively distributed graph generation"):
  with members:
    - "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"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-7 (induced by source paper "lightweight mpi communicators with applications to perfectly balanced quicksort"):
  with members:
    - "robust massively parallel sorting"
    - "practical massively parallel sorting"

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)