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: "optimal adaptive algorithms for finding the nearest and farthest point on a parametric black box curve"
  Abstract: We consider a general model for representing and manipulating parametric curves, in which a curve is specified by a black box mapping a parameter value between 0 and 1 to a point in Euclidean d-space. In this model, we consider the nearest-point-on-curve and farthest-point-on-curve problems: given a curve C and a point p, find a point on C nearest to p or farthest from p. In the general black-box model, no algorithm can solve these problems. Assuming a known bound on the speed of the curve (a Lipschitz condition), the answer can be estimated up to an additive error of epsilon using O(1/epsilon) samples, and this bound is tight in the worst case. However, many instances can be solved with substantially fewer samples, and we give algorithms that adapt to the inherent difficulty of the particular instance, up to a logarithmic factor. More precisely, if OPT(C,p,epsilon) is the minimum number of samples of C that every correct algorithm must perform to achieve tolerance epsilon, then our algorithm performs O(OPT(C,p,epsilon) log (epsilon^(-1)/OPT(C,p,epsilon))) samples. Furthermore, any algorithm requires Omega(k log (epsilon^(-1)/k)) samples for some instance C' with OPT(C',p,epsilon) = k; except that, for the nearest-point-on-curve problem when the distance between C and p is less than epsilon, OPT is 1 but the upper and lower bounds on the number of samples are both Theta(1/epsilon). When bounds on relative error are desired, we give algorithms that perform O(OPT log (2+(1+epsilon^(-1)) m^(-1)/OPT)) samples (where m is the exact minimum or maximum distance from p to C) and prove that Omega(OPT log (1/epsilon)) samples are necessary on some problem instances.

Connected through hyperedge HE-1 (induced by source paper "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "skyline computation with noisy comparisons"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "instance optimal geometric algorithms"

Connected through hyperedge HE-2 (induced by source paper "instance optimal geometric algorithms"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"
    - "self improving algorithms"
    - "odds on trees"
    - "biased range trees"
    - "optimal aggregation algorithms for middleware"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"
    - "dense point sets have sparse delaunay triangulations"

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)