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: "convex hull of imprecise points in o n log n time after preprocessing"
  Abstract: Motivated by the desire to cope with \emph{data imprecision} \cite{Loeffler09}, we study methods for preprocessing a set of planar regions such that whenever we are given a set of points, each of which lies on a distinct region, we can compute a specified structure on these points more efficiently than in "standard settings" (that is, without preprocessing). In particular, we study two related problems. First, given a set $L$ of $n$ lines in the plane, we wish to preprocess $L$ such that later, upon receiving a set $P$ of $n$ points, each of which lies on a distinct line of $L$, we can construct the convex hull of $P$ efficiently. We show that in quadratic time and space it is possible to construct a data structure on $L$ that enables us to compute the convex hull of any such point set $P$ in $O(n \alpha(n) \log^* n)$ expected time. If we further assume that the points are "oblivious" with respect to the data structure, the running time improves to $O(n \alpha(n))$. The analysis applies almost verbatim when $L$ is a set of line-segments, and yields similar asymptotic bounds. We present several extensions, including a trade-off between space and query time and an output-sensitive algorithm. Second, we study the dual setting. Here, we are given a planar $n$-point set $P$, and we wish to preprocess $P$ such that given any set of lines $L$, each of which is incident to a distinct point of $P$, we can compute the $(\leq k)$-level of $L$ efficiently. Again, with quadratic preprocessing and storage, we obtain a data structure for $P$ such that for any such $L$, the $(\leq k)$-level can be constructed in expected time $O(n\alpha(n)(\log^*n - \log^* k) + nk)$. The bound improves to $O(n \alpha(n) + nk)$ if we assume obliviousness.

Connected through hyperedge HE-1 (induced by source paper "sharp bounds on davenport schinzel sequences of every order"):
  with members:
    - "convex hull of points lying on lines in o nlogn time after preprocessing"
    - "complexity of a single face in an arrangement of s intersecting curves"
    - "largest empty circle centered on a query line"
    - "a kinetic triangulation scheme for moving points in the plane"
    - "improved bounds and new techniques for davenport schinzel sequences and their generalizations"
    - "querying two boundary points for shortest paths in a polygonal domain"
    - "splay trees davenport schinzel sequences and the deque conjecture"

Connected through hyperedge HE-2 (induced by source paper "self improving algorithms for coordinate wise maxima and convex hulls"):
  with members:
    - "instance optimal geometric algorithms"
    - "convex hull of points lying on lines in o nlogn time after preprocessing"
    - "self improving algorithms"
    - "odds on trees"

Connected through hyperedge HE-3 (induced by source paper "self improving algorithms for coordinate wise maxima"):
  with members:
    - "instance optimal geometric algorithms"
    - "self improving algorithms"
    - "optimal aggregation algorithms for middleware"

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)