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 "a ptas for euclidean tsp with hyperplane neighborhoods"):
  Abstract: In the Traveling Salesperson Problem with Neighborhoods (TSPN), we are given a collection of geometric regions in some space. The goal is to output a tour of minimum length that visits at least one point in each region. Even in the Euclidean plane, TSPN is known to be APX-hard, which gives rise to studying more tractable special cases of the problem. In this paper, we focus on the fundamental special case of regions that are hyperplanes in the $d$-dimensional Euclidean space. This case contrasts the much-better understood case of so-called fat regions. #R##N#While for $d=2$ an exact algorithm with running time $O(n^5)$ is known, settling the exact approximability of the problem for $d=3$ has been repeatedly posed as an open question. To date, only an approximation algorithm with guarantee exponential in $d$ is known, and NP-hardness remains open. #R##N#For arbitrary fixed $d$, we develop a Polynomial Time Approximation Scheme (PTAS) that works for both the tour and path version of the problem. Our algorithm is based on approximating the convex hull of the optimal tour by a convex polytope of bounded complexity. Such polytopes are represented as solutions of a sophisticated LP formulation, which we combine with the enumeration of crucial properties of the tour. As the approximation guarantee approaches $1$, our scheme adjusts the complexity of the considered polytopes accordingly. #R##N#In the analysis of our approximation scheme, we show that our search space includes a sufficiently good approximation of the optimum. To do so, we develop a novel and general sparsification technique to transform an arbitrary convex polytope into one with a constant number of vertices and, in turn, into one of bounded complexity in the above sense. Hereby, we maintain important properties of the polytope.

Includes members:
  Member "chasing convex bodies optimally":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "competitively chasing convex bodies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "a ptas for tsp with neighborhoods among fat regions in the plane":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved bounds for the traveling salesman problem with neighborhoods on uniform disks")
  Member "nested convex bodies are chaseable":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "path length bounds for gradient descent and flow")
  Member "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a quasi polynomial algorithm for well spaced hyperbolic tsp")
  Member "the traveling salesman problem for lines balls and planes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved bounds for the traveling salesman problem with neighborhoods on uniform disks")
  Member "the traveling salesman problem for lines and rays in the plane":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the traveling salesman problem for lines balls and planes")
  Member "approximation algorithms for tsp with neighborhoods in the plane":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive view planning for aerial 3d reconstruction")

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.DS(Data Structures and Algorithms)