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: "computing shapley values in the plane"
  Abstract: We consider the problem of computing Shapley values for points in the plane, where each point is interpreted as a player, and the value of a coalition is defined by the area of usual geometric objects, such as the convex hull or the minimum axis-parallel bounding box. #R##N#For sets of $n$ points in the plane, we show how to compute in roughly $O(n^{3/2})$ time the Shapley values for the area of the minimum axis-parallel bounding box and the area of the union of the rectangles spanned by the origin and the input points. When the points form an increasing or decreasing chain, the running time can be improved to near-linear. In all these cases, we use linearity of the Shapley values and fast multi-point evaluation of polynomials as a key subroutine. #R##N#We also show that Shapley values for the area and the perimeter of the convex hull or the minimum enclosing disk can be computed in polynomial time. In this case the computation is closely related to the model of stochastic point sets considered in computational geometry, but here we have to consider random insertion orders of the points instead of a probabilistic existence of points.

Connected through hyperedge HE-1 (induced by source paper "computing shapley values for mean width in 3 d"):
  with members:
    - "on the expected diameter width and complexity of a stochastic convex hull"
    - "epsilon kernel coresets for stochastic points"
    - "batched point location in sinr diagrams via algebraic tools"
    - "convex hulls under uncertainty"

Connected through hyperedge HE-2 (induced by source paper "computing the value of spatio temporal data in wholesale and retail data marketplaces"):
  with members:
    - "valuating user data in a human centric data economy"
    - "a marketplace for data an algorithmic solution"
    - "optimal mechanisms for selling information"
    - "the cost of stability in coalitional games"

Connected through hyperedge HE-3 (induced by source paper "valuating user data in a human centric data economy"):
  with members:
    - "from advertising profits to bandwidth prices a quantitative methodology for negotiating premium peering"
    - "your browsing behavior for a big mac economics of personal information online"
    - "the cost of stability in coalitional games"

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)