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: "coast a convex optimization approach to stress based embedding"
  Abstract: Visualizing graphs using virtual physical models is probably the most heavily used technique for drawing graphs in practice. There are many algorithms that are efficient and produce high-quality layouts. If one requires that the layout also respect a given set of non-uniform edge lengths, however, force-based approaches become problematic while energy-based layouts become intractable. In this paper, we propose a reformulation of the stress function into a two-part convex objective function to which we can apply semi-definite programming (SDP). We avoid the high computational cost associated with SDP by a novel, compact re-parameterization of the objective function using the eigenvectors of the graph Laplacian. This sparse representation makes our approach scalable. We provide experimental results to show that this method scales well and produces reasonable layouts while dealing with the edge length constraints.

Connected through hyperedge HE-1 (induced by source paper "inceptiongcn receptive field aware graph convolutional network for disease prediction"):
  with members:
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "representation learning on graphs with jumping knowledge networks"
    - "multi layered parallel graph convolutional network ml pgcn for disease prediction"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "densely connected convolutional networks"
    - "semi supervised classification with graph convolutional networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "multi level tree based approach for interactive graph visualization with semantic zoom"):
  with members:
    - "multi level steiner trees"
    - "inka an ink based model of graph visualization"
    - "a new approach to graphmaps a system browsing large graphs as interactive maps"
    - "node overlap removal by growing a tree"
    - "node node link and node link group diagrams an evaluation"

Connected through hyperedge HE-3 (induced by source paper "a sparse stress model"):
  with members:
    - "drawing large graphs by multilevel maxent stress optimization"

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"
    - "a sparse stress model"
    - "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"
    - "partitioning complex networks via size constrained clustering"
    - "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.DM(Discrete Mathematics)