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: "robust geometric spanners"
  Abstract: Highly connected and yet sparse graphs (such as expanders or graphs of high treewidth) are fundamental, widely applicable and extensively studied combinatorial objects. We initiate the study of such highly connected graphs that are, in addition, geometric spanners. We define a property of spanners called robustness. Informally, when one removes a few vertices from a robust spanner, this harms only a small number of other vertices. We show that robust spanners must have a superlinear number of edges, even in one dimension. On the positive side, we give constructions, for any dimension, of robust spanners with a near-linear number of edges.

Connected through hyperedge HE-1 (induced by source paper "piecewise linear regressions for approximating distance metrics"):
  with members:
    - "a survey on network embedding"

Connected through hyperedge HE-2 (induced by source paper "sometimes reliable spanners of almost linear size"):
  with members:
    - "a spanner for the day after"
    - "on locality sensitive orderings and their applications"
    - "near optimal o k robust geometric spanners"

Connected through hyperedge HE-3 (induced by source paper "a spanner for the day after"):
  with members:
    - "on locality sensitive orderings and their applications"
    - "near optimal o k robust geometric spanners"
    - "sparse geometric graphs with small dilation"

Connected through hyperedge HE-4 (induced by source paper "near optimal o k robust geometric spanners"):
  with members:
    - "on locality sensitive orderings and their applications"

Connected through hyperedge HE-5 (induced by source paper "the minimum edge compact spanner network design problem"):
  with members:
    - "graph measures and network robustness"
    - "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"
    - "fault tolerant spanners for doubling metrics better and simpler"
    - "fault tolerant spanners better and simpler"

Connected through hyperedge HE-6 (induced by source paper "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"):
  with members:
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "sparse fault tolerant bfs trees"
    - "efficient construction of spanners in d dimensions"
    - "on resilient graph spanners"
    - "fault tolerant spanners for doubling metrics better and simpler"
    - "optimal euclidean spanners really short thin and lanky"
    - "incubators vs zombies fault tolerant short thin and lanky spanners for doubling metrics"
    - "balancing degree diameter and weight in euclidean spanners"

Connected through hyperedge HE-7 (induced by source paper "on resilient graph spanners"):
  with members:
    - "fault tolerant spanners better and simpler"
    - "adaptive uncertainty resolution in bayesian combinatorial optimization problems"

Connected through hyperedge HE-8 (induced by source paper "average stretch factor how low does it go"):
  with members:
    - "embedding metrics into ultrametrics and graphs into spanning trees with constant average distortion"

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)