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: "shape fitting on point sets with probability distributions"
  Abstract: A typical computational geometry problem begins: Consider a set P of n points in R^d. However, many applications today work with input that is not precisely known, for example when the data is sensed and has some known error model. What if we do not know the set P exactly, but rather we have a probability distribution mu_p governing the location of each point p in P? #R##N#Consider a set of (non-fixed) points P, and let mu_P be the probability distribution of this set. We study several measures (e.g. the radius of the smallest enclosing ball, or the area of the smallest enclosing box) with respect to mu_P. The solutions to these problems do not, as in the traditional case, consist of a single answer, but rather a distribution of answers. We describe several data structures that approximate distributions of answers for shape fitting problems. #R##N#We provide simple and efficient randomized algorithms for computing all of these data structures, which are easy to implement and practical. We provide some experimental results to assert this. We also provide more involved deterministic algorithms for some of these data structures that run in time polynomial in n and 1/eps, where eps is the approximation factor.

Connected through hyperedge HE-1 (induced by source paper "shortest path centrality and the all pairs shortest paths problem via vc dimension theory"):
  with members:
    - "estimating the percolation centrality of large networks through pseudo dimension theory"
    - "almost shortest paths and pram distance oracles in weighted graphs"
    - "approximating apsp without scaling equivalence of approximate min plus and exact min max"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "on the shoshan zwick algorithm for the all pairs shortest path problem"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "faster all pairs shortest paths via circuit complexity"

Connected through hyperedge HE-2 (induced by source paper "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"):
  with members:
    - "refining approximating betweenness centrality based on samplings"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "approximating betweenness centrality in fully dynamic networks"
    - "fully dynamic approximation of betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"

Connected through hyperedge HE-3 (induced by source paper "region based approximation of probability distributions for visibility between imprecise points among obstacles"):
  with members:
    - "histograms and wavelets on probabilistic data"

Connected through hyperedge HE-4 (induced by source paper "range counting coresets for uncertain data"):
  with members:
    - "geometric computations on indecisive and uncertain points"
    - "histograms and wavelets on probabilistic data"
    - "algorithms for eps approximations of terrains"

Connected through hyperedge HE-5 (induced by source paper "finding the true frequent itemsets"):
  with members:
    - "controlling false positives in association rule mining"
    - "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"
    - "an efficient rigorous approach for identifying statistically significant frequent itemsets"

Connected through hyperedge HE-6 (induced by source paper "convex hull of points lying on lines in o nlogn time after preprocessing"):
  with members:
    - "instance optimal geometric algorithms"
    - "triangulating the square and squaring the triangle quadtrees and delaunay triangulations are equivalent"
    - "randomization yields simple o n log star n algorithms for difficult omega n problems"

Connected through hyperedge HE-7 (induced by source paper "approximating the expected values for combinatorial optimization problems over stochastic points"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "convex hulls under uncertainty"
    - "computational transition at the uniqueness threshold"
    - "approximating the statistics of various properties in randomly weighted graphs"

Connected through hyperedge HE-8 (induced by source paper "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"):
  with members:
    - "a learning theory approach to non interactive database privacy"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "the vc dimension of queries and selectivity estimation through sampling"
    - "mining top k frequent itemsets through progressive sampling"

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)