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: "communication free massively distributed graph generation"
  Abstract: Analyzing massive complex networks yields promising insights about our everyday lives. Building scalable algorithms to do that is a challenging task that requires a careful analysis and extensive evaluation. However, engineering such algorithms is often hindered by the scarcity of publicly available datasets. Network generators serve as a tool to alleviate this problem by providing synthetic instances with controllable parameters. However, many network generators fail to provide instances on a massive scale due to their sequential nature or resource constraints. Additionally, truly scalable network generators are few and often limited in their realism. In this work, we present novel generators for a variety of network models commonly found in practice. By making use of pseudorandomization and divide-and-conquer schemes, our generators follow a communication-free paradigm, i.e. they require no communication. The resulting generators are often embarrassingly parallel and have a near optimal scaling behavior. Overall, we are able to generate instances of up to $2^{43}$ vertices and $2^{47}$ edges in less than 22 minutes on 32768 processors. Therefore, our generators allow new graph families to be used on an unprecedented scale.

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 "artificial benchmark for community detection abcd fast random graph model with community structure"):
  with members:
    - "community structure a comparative evaluation of community detection methods"
    - "the switch markov chain for sampling irregular graphs and digraphs"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "julia a fresh approach to numerical computing"
    - "a scalable generative graph model with community structure"
    - "are we there yet when to stop a markov chain while generating random graphs"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-3 (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-4 (induced by source paper "shared memory branch and reduce for multiterminal cuts"):
  with members:
    - "ilp based local search for graph partitioning"
    - "practical minimum cut algorithms"
    - "shared memory exact minimum cuts"
    - "local flow partitioning for faster edge connectivity"
    - "an o 1 84 k parameterized algorithm for the multiterminal cut problem"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-5 (induced by source paper "linear work generation of r mat graphs"):
  with members:
    - "parallel weighted random sampling"
    - "scalable generation of scale free graphs"
    - "networkit a tool suite for large scale complex network analysis"

Connected through hyperedge HE-6 (induced by source paper "balanced k means for parallel geometric partitioning"):
  with members:
    - "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"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-7 (induced by source paper "scalable edge partitioning"):
  with members:
    - "social hash partitioner a scalable distributed hypergraph partitioner"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-8 (induced by source paper "efficient parallel random sampling vectorized cache efficient and online"):
  with members:
    - "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.DC(Distributed, Parallel, and Cluster Computing)