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 "learning navigation costs from demonstration in partially observable environments"):
  Abstract: This paper focuses on inverse reinforcement learning (IRL) to enable safe and efficient autonomous navigation in unknown partially observable environments. The objective is to infer a cost function that explains expert-demonstrated navigation behavior while relying only on the observations and state-control trajectory used by the expert. We develop a cost function representation composed of two parts: a probabilistic occupancy encoder, with recurrent dependence on the observation sequence, and a cost encoder, defined over the occupancy features. The representation parameters are optimized by differentiating the error between demonstrated controls and a control policy computed from the cost encoder. Such differentiation is typically computed by dynamic programming through the value function over the whole state space. We observe that this is inefficient in large partially observable environments because most states are unexplored. Instead, we rely on a closed-form subgradient of the cost-to-go obtained only over a subset of promising states via an efficient motion-planning algorithm such as A* or RRT. Our experiments show that our model exceeds the accuracy of baseline IRL algorithms in robot navigation tasks, while substantially improving the efficiency of training and test-time inference.

Includes members:
  Member "differentiable algorithm networks for composable robot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning task driven control policies via information bottlenecks")
  Member "path integral networks end to end differentiable optimal control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference")
  Member "memory augmented control networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sufficiently accurate model learning")
  Member "qmdp net deep learning for planning under partial observability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "reinforcement learning via recurrent convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dual sequential monte carlo tunneling filtering and planning in continuous pomdps")
  Member "cognitive mapping and planning for visual navigation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "watch this scalable cost function learning for path planning in urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "value iteration networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")

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.LG(Machine Learning)