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: "aggregative coarsening for multilevel hypergraph partitioning"
  Abstract: Algorithms for many hypergraph problems, including partitioning, utilize multilevel frameworks to achieve a good trade-off between the performance and the quality of results. In this paper we introduce two novel aggregative coarsening schemes and incorporate them within state-of-the-art hypergraph partitioner Zoltan. Our coarsening schemes are inspired by the algebraic multigrid and stable matching approaches. We demonstrate the effectiveness of the developed schemes as a part of multilevel hypergraph partitioning framework on a wide range of problems. Reproducibility: [link to code, data and results will be available upon acceptance of the paper]

Connected through hyperedge HE-1 (induced by source paper "hypergraph partitioning with embeddings"):
  with members:
    - "pytorch biggraph a large scale graph embedding system"
    - "verse versatile graph embeddings from similarity measures"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "memetic multilevel hypergraph partitioning"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "k way hypergraph partitioning via n level recursive bisection"

Connected through hyperedge HE-2 (induced by source paper "partition hypergraphs with embeddings"):
  with members:
    - "fobe and hobe first and high order bipartite embeddings"
    - "memetic multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "advanced coarsening schemes for graph partitioning"

Connected through hyperedge HE-3 (induced by source paper "network flow based refinement for multilevel hypergraph partitioning"):
  with members:
    - "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"
    - "partitioning complex networks via size constrained clustering"
    - "engineering multilevel graph partitioning algorithms"
    - "benchmarking optimization software with performance profiles"

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)