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: "a sparse stress model"
  Abstract: Force-directed layout methods constitute the most common approach to draw general graphs. Among them, stress minimization produces layouts of comparatively high quality but also imposes comparatively high computational demands. We propose a speed-up method based on the aggregation of terms in the objective function. It is akin to aggregate repulsion from far-away nodes during spring embedding but transfers the idea from the layout space into a preprocessing phase. An initial experimental study informs a method to select representatives, and subsequent more extensive experiments indicate that our method yields better approximations of minimum-stress layouts in less time than related methods.

Connected through hyperedge HE-1 (induced by source paper "a quality metric for visualization of clusters in graphs"):
  with members:
    - "group level graph visualization taxonomy"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-2 (induced by source paper "graph drawing by stochastic gradient descent"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "a fast successive over relaxation algorithm for force directed network graph drawing"

Connected through hyperedge HE-3 (induced by source paper "a distributed multilevel force directed algorithm"):
  with members:
    - "a distributed force directed algorithm on giraph design and experiments"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "parallel graph partitioning for complex networks"

Connected through hyperedge HE-4 (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"
    - "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"
    - "partitioning complex networks via size constrained clustering"
    - "coast a convex optimization approach to stress based embedding"
    - "engineering a scalable high quality graph partitioner"

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.CG(Computational Geometry)