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: "an efficient algorithm for computing high quality paths amid polygonal obstacles"
  Abstract: We study a path-planning problem amid a set $\mathcal{O}$ of obstacles in $\mathbb{R}^2$, in which we wish to compute a short path between two points while also maintaining a high clearance from $\mathcal{O}$; the clearance of a point is its distance from a nearest obstacle in $\mathcal{O}$. Specifically, the problem asks for a path minimizing the reciprocal of the clearance integrated over the length of the path. We present the first polynomial-time approximation scheme for this problem. Let $n$ be the total number of obstacle vertices and let $\varepsilon \in (0,1]$. Our algorithm computes in time $O(\frac{n^2}{\varepsilon ^2} \log \frac{n}{\varepsilon})$ a path of total cost at most $(1+\varepsilon)$ times the cost of the optimal path.

Connected through hyperedge HE-1 (induced by source paper "geometric secluded paths and planar satisfiability"):
  with members:
    - "on the computational complexity of length and neighborhood constrained path problems"
    - "on the complexity of minimum link path problems"
    - "quickest visibility queries in polygonal domains"
    - "planar 3 sat with a clause variable cycle"
    - "a polynomial time algorithm for finding an approximate shortest path amid weighted regions"
    - "parameterized complexity of secluded connectivity problems"
    - "a note on the unsolvability of the weighted region shortest path problem"
    - "minimum link paths revisited"

Connected through hyperedge HE-2 (induced by source paper "efficient sampling based bottleneck pathfinding over cost maps"):
  with members:
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "sampling based bottleneck pathfinding with applications to frechet matching"
    - "time optimal coordination of mobile robots along specified paths"
    - "on the complexity of an unregulated traffic crossing"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"

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)