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: "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.

Connected through hyperedge HE-1 (induced by source paper "dynamic distribution sensitive point location"):
  with members:
    - "point location in incremental planar subdivisions"
    - "point location in dynamic planar subdivisions"
    - "adaptive planar point location"
    - "entropy triangulation and point location in planar subdivisions"
    - "a static optimality transformation with applications to planar point location"
    - "odds on trees"

Connected through hyperedge HE-2 (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"
    - "optimal adaptive algorithms for finding the nearest and farthest point on a parametric black box curve"

Connected through hyperedge HE-3 (induced by source paper "depth distribution in high dimensions"):
  with members:
    - "joins via geometric resolutions worst case and beyond"
    - "powers of tensors and fast matrix multiplication"
    - "bringing order to special cases of klee s measure problem"

Connected through hyperedge HE-4 (induced by source paper "multivariate analysis for computing maxima in high dimensions"):
  with members:
    - "near optimal online multiselection in internal and external memory"

Connected through hyperedge HE-5 (induced by source paper "towards instance optimal bounds for best arm identification"):
  with members:
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "on the optimal sample complexity for best arm identification"
    - "multiple identifications in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "open problem best arm identification almost instance wise optimality and the gap entropy conjecture"):
  with members:
    - "on the optimal sample complexity for best arm identification"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-7 (induced by source paper "optimal prefix free codes with partial sorting"):
  with members:
    - "on compressing permutations and adaptive sorting"

Connected through hyperedge HE-8 (induced by source paper "on the optimal sample complexity for best arm identification"):
  with members:
    - "pure exploration of multi armed bandit under matroid constraints"
    - "multiple identifications in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "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)