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: "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
  Abstract: While the shortest path problem has myriad applications, the computational efficiency of suitable algorithms depends intimately on the underlying problem domain. In this paper, we focus on domains where evaluating the edge weight function dominates algorithm running time. Inspired by approaches in robotic motion planning, we define and investigate the Lazy Shortest Path class of algorithms which is differentiated by the choice of an edge selector function. We show that several algorithms in the literature are equivalent to this lazy algorithm for appropriate choice of this selector. Further, we propose various novel selectors inspired by sampling and statistical mechanics, and find that these selectors outperform existing algorithms on a set of example problems.

Connected through hyperedge HE-1 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges"):
  with members:
    - "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"
    - "leveraging experience in lazy search"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "motion planning networks"
    - "bayesian active edge evaluation on expensive graphs"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "pomdp lite for robust robot planning under uncertainty"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics"):
  with members:
    - "batch informed trees bit informed asymptotically optimal anytime search"
    - "sampling based optimal kinodynamic planning with motion primitives"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "informed sampling for asymptotically optimal path planning"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "fast planning over roadmaps via selective densification"):
  with members:
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "the provable virtue of laziness in motion planning"
    - "anytime motion planning on large dense roadmaps with expensive edge evaluations"
    - "densification strategies for anytime motion planning over large dense roadmaps"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "an asymptotically optimal sampling based algorithm for bi directional motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "leveraging experience in lazy search"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "fast policy learning through imitation and reinforcement"
    - "the provable virtue of laziness in motion planning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "bayesian active edge evaluation on expensive graphs"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "learning heuristic search via imitation"

Connected through hyperedge HE-5 (induced by source paper "toward asymptotically optimal inspection planning via efficient near optimal graph search"):
  with members:
    - "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "efficient motion planning for problems lacking optimal substructure"
    - "the provable virtue of laziness in motion planning"
    - "online interactive user guidance for high dimensional constrained motion planning"
    - "effective footstep planning for humanoids using homotopy class guidance"
    - "new perspective on sampling based motion planning via random geometric graphs"

Connected through hyperedge HE-6 (induced by source paper "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"):
  with members:
    - "occlusion aware risk assessment for autonomous driving in urban environments"
    - "identifying robust landmarks in feature based maps"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "on a formal model of safe and scalable self driving cars"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-7 (induced by source paper "sampling based methods for factored task and motion planning"):
  with members:
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "strips planning in infinite domains"
    - "backward forward search for manipulation planning"
    - "towards combining htn planning and geometric task planning"
    - "sampling based algorithms for optimal motion planning"
    - "the fast downward planning system"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-8 (induced by source paper "perception driven sparse graphs for optimal motion planning"):
  with members:
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "sampling based algorithms for optimal motion planning"
    - "theta any angle path planning on grids"

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)