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: "on the expected complexity of random convex hulls"
  Abstract: In this paper we present several results on the expected complexity of a convex hull of $n$ points chosen uniformly and independently from a convex shape. #R##N#(i) We show that the expected number of vertices of the convex hull of $n$ points, chosen uniformly and independently from a disk is $O(n^{1/3})$, and $O(k \log{n})$ for the case a convex polygon with $k$ sides. Those results are well known (see \cite{rs-udkhv-63,r-slcdn-70,ps-cgi-85}), but we believe that the elementary proof given here are simpler and more intuitive. #R##N#(ii) Let $\D$ be a set of directions in the plane, we define a generalized notion of convexity induced by $\D$, which extends both rectilinear convexity and standard convexity. #R##N#We prove that the expected complexity of the $\D$-convex hull of a set of $n$ points, chosen uniformly and independently from a disk, is $O(n^{1/3} + \sqrt{n\alpha(\D)})$, where $\alpha(\D)$ is the largest angle between two consecutive vectors in $\D$. This result extends the known bounds for the cases of rectilinear and standard convexity. #R##N#(iii) Let $\B$ be an axis parallel hypercube in $\Re^d$. We prove that the expected number of points on the boundary of the quadrant hull of a set $S$ of $n$ points, chosen uniformly and independently from $\B$ is $O(\log^{d-1}n)$. Quadrant hull of a set of points is an extension of rectilinear convexity to higher dimensions. In particular, this number is larger than the number of maxima in $S$, and is also larger than the number of points of $S$ that are vertices of the convex hull of $S$. #R##N#Those bounds are known \cite{bkst-anmsv-78}, but we believe the new proof is simpler.

Connected through hyperedge HE-1 (induced by source paper "convex hulls of random order types"):
  with members:
    - "chirotopes of random points in space are realizable on a small integer grid"
    - "smoothed analysis of order types"
    - "on the average complexity of the k level"

Connected through hyperedge HE-2 (induced by source paper "discriminative training of conditional random fields with probably submodular constraints"):
  with members:
    - "fast mitochondria segmentation for connectomics"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "learning deep structured models"

Connected through hyperedge HE-3 (induced by source paper "on the average complexity of the k level"):
  with members:
    - "convex hulls of random order types"
    - "large convex holes in random point sets"

Connected through hyperedge HE-4 (induced by source paper "rrr rank regret representative"):
  with members:
    - "qr2 a third party query reranking service over web databases"
    - "efficient algorithms for k regret minimizing sets"
    - "query reranking as a service"
    - "discovering the skyline of web databases"
    - "crowdsourcing pareto optimal object finding by pairwise comparisons"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-5 (induced by source paper "on separating points by lines"):
  with members:
    - "peeling the grid"
    - "on range searching with semialgebraic sets ii"

Connected through hyperedge HE-6 (induced by source paper "steering a particle swarm using global inputs and swarm statistics"):
  with members:
    - "crowdsourcing swarm manipulation experiments a massive online user study with large swarms of simple robots"
    - "a compliant underactuated hand for robust manipulation"

Connected through hyperedge HE-7 (induced by source paper "searching for a compressed polyline with a minimum number of vertices discrete solution"):
  with members:
    - "optimal compression of a polyline with segments and arcs"

Connected through hyperedge HE-8 (induced by source paper "epsilon kernel coresets for stochastic points"):
  with members:
    - "range queries on uncertain data"
    - "convex hulls under uncertainty"
    - "range counting coresets for uncertain data"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"
    - "flow computations on imprecise terrains"
    - "a unified framework for approximating and clustering data"

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)