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: "bit batch informed trees for optimal sampling based planning via dynamic programming on implicit random geometric graphs"
  Abstract: Discrete and sampling-based methods have traditionally been popular techniques for path planning in continuous spaces. Discrete techniques use the principles of dynamic programming to solve a discretized approximation of the problem, while sampling-based techniques use random samples to perform a stochastic search on the continuous state space. #R##N#In this paper, we use the fact that stochastic planners can be viewed as a search of an implicit random geometric graph (RGG) to propose a general class of planners named Bellman Random Trees (BRT) and derive an anytime optimal sampling-based algorithm, Batch Informed Trees (BIT*). #R##N#BIT* searches for a solution to the continuous planning problem by efficiently building and searching a series of implicit RGGs in a principled manner. In doing so, it strikes a balance between the advantages of discrete methods and sampling-based planners. By using the implicit RGG representation, defined as set of random samples and successor function, BIT* is able to scale more effectively to high dimensions than other optimal sampling-based planners. By using heuristics and intelligently reusing existing connections, like discrete lifelong planning algorithms, BIT* is able to focus its search in a principled and efficient manner. #R##N#In simulations, we show that BIT* consistently outperforms Optimal RRT (RRT*), Informed RRT*, and Fast Marching Trees (FMT*) on random-world problems in $\mathbb{R}^2$ and $\mathbb{R}^8$ . We also present preliminary theoretical analysis demonstrating that BIT* is probabilistically complete and asymptotically optimal and conjecture that it may be optimally efficient under some probabilistic conditions.

Connected through hyperedge HE-1 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "incremental sampling based motion planners using policy iteration methods"
    - "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"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "efficient high quality motion planning by fast all pairs r nearest neighbors"
    - "asymptotically near optimal rrt for fast high quality motion planning"

Connected through hyperedge HE-2 (induced by source paper "sampling based optimal kinodynamic planning with motion primitives"):
  with members:
    - "asymptotically optimal sampling based kinodynamic planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "configuration space metrics"):
  with members:
    - "fast marching trees a fast marching sampling based method for optimal motion planning in many dimensions extended version"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "planning nonlinear access paths for temporal bone surgery"):
  with members:
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "informed asymptotically optimal anytime search"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "incremental sampling based motion planners using policy iteration methods"
    - "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"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "asymptotically near optimal rrt for fast high quality motion planning"

Connected through hyperedge HE-6 (induced by source paper "anytime motion planning on large dense roadmaps with expensive edge evaluations"):
  with members:
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "asymptotically near optimal rrt for fast high quality motion planning"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "integrating asymptotically optimal path planning with local optimization"):
  with members:
    - "interleaving optimization with sampling based motion planning ios mp combining local optimization with global exploration"
    - "lessons from the amazon picking challenge"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "asymptotically near optimal rrt for fast high quality motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "towards fully environment aware uavs real time path planning with online 3d wind field prediction in complex terrain"):
  with members:
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "fast marching trees a fast marching sampling based method for optimal motion planning in many dimensions extended version"
    - "sampling based algorithms for optimal motion planning"

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.RO(Robotics)