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: "near optimal edge evaluation in explicit generalized binomial graphs"
  Abstract: Robotic motion-planning problems, such as a UAV flying fast in a partially-known environment or a robot arm moving around cluttered objects, require finding collision-free paths quickly. Typically, this is solved by constructing a graph, where vertices represent robot configurations and edges represent potentially valid movements of the robot between these configurations. The main computational bottlenecks are expensive edge evaluations to check for collisions. State of the art planning methods do not reason about the optimal sequence of edges to evaluate in order to find a collision free path quickly. In this paper, we do so by drawing a novel equivalence between motion planning and the Bayesian active learning paradigm of decision region determination (DRD). Unfortunately, a straight application of existing methods requires computation exponential in the number of edges in a graph. We present BISECT, an efficient and near-optimal algorithm to solve the DRD problem when edges are independent Bernoulli random variables. By leveraging this property, we are able to significantly reduce computational complexity from exponential to linear in the number of edges. We show that BISECT outperforms several state of the art algorithms on a spectrum of planning problems for mobile robots, manipulators, and real flight data collected from a full scale helicopter.

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"
    - "pomdp lite for robust robot planning under uncertainty"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-2 (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"
    - "learning heuristic search via imitation"
    - "data driven planning via imitation learning"

Connected through hyperedge HE-3 (induced by source paper "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"):
  with members:
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "bayesian active edge evaluation on expensive graphs"

Connected through hyperedge HE-4 (induced by source paper "data driven planning via imitation learning"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "deep reinforcement learning a brief survey"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "learning sampling distributions for robot motion planning"
    - "qmdp net deep learning for planning under partial observability"
    - "learning from the hindsight plan episodic mpc improvement"
    - "deep reinforcement learning for dialogue generation"
    - "pomdp lite for robust robot planning under uncertainty"

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)