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: "necklaces convolutions and x y"
  Abstract: We give subquadratic algorithms that, given two necklaces each with n beads at arbitrary positions, compute the optimal rotation of the necklaces to best align the beads. Here alignment is measured according to the p norm of the vector of distances between pairs of beads from opposite necklaces in the best perfect matching. We show surprisingly different results for p = 1, p even, and p = \infty. For p even, we reduce the problem to standard convolution, while for p = \infty and p = 1, we reduce the problem to (min, +) convolution and (median, +) convolution. Then we solve the latter two convolution problems in subquadratic time, which are interesting results in their own right. These results shed some light on the classic sorting X + Y problem, because the convolutions can be viewed as computing order statistics on the antidiagonals of the X + Y matrix. All of our algorithms run in o(n^2) time, whereas the obvious algorithms for these problems run in \Theta(n^2) time.

Connected through hyperedge HE-1 (induced by source paper "an improved fptas for 0 1 knapsack"):
  with members:
    - "on problems equivalent to min convolution"
    - "subquadratic approximation scheme for partition"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "a faster fptas for the unbounded knapsack problem"
    - "faster all pairs shortest paths via circuit complexity"

Connected through hyperedge HE-2 (induced by source paper "linear time approximation schemes for geometric maximum coverage"):
  with members:
    - "a ptas for the weighted unit disk cover problem"
    - "faster all pairs shortest paths via circuit complexity"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-3 (induced by source paper "decoding hidden markov models faster than viterbi via online matrix vector max multiplication"):
  with members:
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"

Connected through hyperedge HE-4 (induced by source paper "four soviets walk the dog with an application to alt s conjecture"):
  with members:
    - "computing the frechet distance with a retractable leash"
    - "how to walk your dog in the mountains with no magic leash"
    - "locally correct frechet matchings"
    - "on range searching with semialgebraic sets ii"
    - "computing the discrete fr echet distance in subquadratic time"
    - "can we compute the similarity between surfaces"

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.DS(Data Structures and Algorithms)