human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "poddp partially observable differential dynamic programming for latent belief space planning"):
  Abstract: Autonomous agents are limited in their ability to observe the world state. Partially observable Markov decision processes (POMDPs) formally model the problem of planning under world state uncertainty, but POMDPs with continuous actions and nonlinear dynamics suitable for robotics applications are challenging to solve. In this paper, we present an efficient differential dynamic programming (DDP) algorithm for belief space planning in POMDPs with uncertainty over a discrete latent state, and continuous states, actions, observations, and nonlinear dynamics. This representation allows planning of dynamic trajectories which are sensitive to structured uncertainty over discrete latent world states. We develop dynamic programming techniques to optimize a contingency plan over a tree of possible observations and belief space trajectories, and also derive a hierarchical version of the algorithm. Our method is applicable to problems with uncertainty over the cost or reward function (e.g., the configuration of goals or obstacles), uncertainty over the dynamics (e.g., the dynamical mode of a hybrid system), and uncertainty about interactions, where other agents' behavior is conditioned on latent intentions. Benchmarks show that our algorithm outperforms popular heuristic approaches to planning under uncertainty, and results from an autonomous lane changing task demonstrate that our algorithm can synthesize robust interactive trajectories.

Includes members:
  Member "hyp despot a hybrid parallel algorithm for online planning under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching")
  Member "hierarchical game theoretic planning for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points")
  Member "deep variational reinforcement learning for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "the value of inferring the internal state of traffic participants for autonomous freeway driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "despot online pomdp planning with regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching")
  Member "pomdp lite for robust robot planning under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "past present and future of simultaneous localization and mapping toward the robust perception age":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "a scalable method for solving high dimensional continuous pomdps using local approximation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "perception aware autonomous mast motion planning for planetary exploration rovers")

Question: Please tell me which class the source paper 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)