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: "bi directional value learning for risk aware planning under uncertainty"
  Abstract: Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a partially observable Markov decision process (POMDP). The solution policy of a POMDP can be implicitly encoded as a value function. In partially observable settings, the value function is typically learned via forward simulation of the system evolution. Focusing on accurate and long-range risk assessment, we propose a novel method, where the value function is learned in different phases via a bi-directional search in belief space. A backward value learning process provides a long-range and risk-aware base policy. A forward value learning process ensures local optimality and updates the policy via forward simulations. We consider a class of scalable and continuous-space rover navigation problems to assess the safety, scalability, and optimality of the proposed algorithm. The results demonstrate the capabilities of the proposed algorithm in evaluating long-range risk/safety of the planner while addressing continuous problems with long planning horizons.

Connected through hyperedge HE-1 (induced by source paper "the shapeshifter a morphing multi agent multi modal robotic platform for the exploration of titan preprint version"):
  with members:
    - "robust collaborative object transportation using multiple mavs"
    - "real time stochastic kinodynamic motion planning via multiobjective search on gpus"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"

Connected through hyperedge HE-2 (induced by source paper "perception aware autonomous mast motion planning for planetary exploration rovers"):
  with members:
    - "low level active visual navigation increasing robustness of vision based localization using potential fields"
    - "despot online pomdp planning with regularization"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"
    - "efficient planning under uncertainty with macro actions"
    - "sampling based algorithms for optimal motion planning"
    - "perseus randomized point based value iteration for pomdps"

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)