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: "approximating minimization diagrams and generalized proximity search"
  Abstract: We investigate the classes of functions whose minimization diagrams can be approximated efficiently in \Re^d. We present a general framework and a data-structure that can be used to approximate the minimization diagram of such functions. The resulting data-structure has near linear size and can answer queries in logarithmic time. Applications include approximating the Voronoi diagram of (additively or multiplicatively) weighted points. Our technique also works for more general distance functions, such as metrics induced by convex bodies, and the nearest furthest-neighbor distance to a set of point sets. Interestingly, our framework works also for distance functions that do not comply with the triangle inequality. For many of these functions no near-linear size approximation was known before.

Connected through hyperedge HE-1 (induced by source paper "resolving sinr queries in a dynamic setting"):
  with members:
    - "dynamic geometric data structures via shallow cuttings"
    - "dynamic planar voronoi diagrams for general distance functions and their algorithmic applications"
    - "batched point location in sinr diagrams via algebraic tools"
    - "sinr diagram with interference cancellation"
    - "the topology of wireless communication"

Connected through hyperedge HE-2 (induced by source paper "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"):
  with members:
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-3 (induced by source paper "batched point location in sinr diagrams via algebraic tools"):
  with members:
    - "sinr diagram with interference cancellation"
    - "computing the distance between piecewise linear bivariate functions"
    - "the topology of wireless communication"
    - "wireless capacity with oblivious power in general metrics"
    - "a constant factor approximation for wireless capacity maximization with power control in the sinr model"
    - "wireless scheduling with power control"
    - "fast multipoint evaluation of bivariate polynomials"

Connected through hyperedge HE-4 (induced by source paper "robust proximity search for balls using sublinear space"):
  with members:
    - "down the rabbit hole robust proximity search and density estimation in sublinear space"

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)