human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "instance optimal geometric algorithms"):
  Abstract: We prove the existence of an algorithm $A$ for computing 2-d or 3-d convex hulls that is optimal for every point set in the following sense: for every sequence $\sigma$ of $n$ points and for every algorithm $A'$ in a certain class $\mathcal{A}$, the running time of $A$ on input $\sigma$ is at most a constant factor times the maximum running time of $A'$ on the worst possible permutation of $\sigma$ for $A'$. We establish a stronger property: for every sequence $\sigma$ of points and every algorithm $A'$, the running time of $A$ on $\sigma$ is at most a constant factor times the average running time of $A'$ over all permutations of $\sigma$. We call algorithms satisfying these properties instance-optimal in the order-oblivious and random-order setting. Such instance-optimal algorithms simultaneously subsume output-sensitive algorithms and distribution-dependent average-case algorithms, and all algorithms that do not take advantage of the order of the input or that assume the input is given in a random order. The class $\mathcal{A}$ under consideration consists of all algorithms in a decision tree model where the tests involve only multilinear functions with a constant number of arguments. To establish an instance-specific lower bound, we deviate from traditional Ben-Or-style proofs and adopt a new adversary argument. For 2-d convex hulls, we prove that a version of the well known algorithm by Kirkpatrick and Seidel (1986) or Chan, Snoeyink, and Yap (1995) already attains this lower bound. For 3-d convex hulls, we propose a new algorithm. We further obtain instance-optimal results for a few other standard problems in computational geometry. Our framework also reveals connection to distribution-sensitive data structures and yields new results as a byproduct, for example, on on-line orthogonal range searching in 2-d and on-line halfspace range reporting in 2-d and 3-d.

Includes members:
  Member "entropy triangulation and point location in planar subdivisions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic distribution sensitive point location")
  Member "self improving algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learned frequency estimation algorithms under zipfian distribution")
  Member "odds on trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic distribution sensitive point location")
  Member "biased range trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear space data structures for range mode query in arrays")
  Member "optimal adaptive algorithms for finding the nearest and farthest point on a parametric black box curve":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in")
  Member "optimal aggregation algorithms for middleware":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding interesting frames in deep video analytics a top k approach")
  Member "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improper learning for non stochastic control")
  Member "dense point sets have sparse delaunay triangulations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "tcm noma joint multi user codeword design and detection in trellis coded modulation based noma for beyond 5g")

Question: Please tell me which class the source paper 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)