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 hybrid object kinematics for efficient hierarchical planning under uncertainty"):
  Abstract: Sudden changes in the dynamics of robotic tasks, such as contact with an object or the latching of a door, are often viewed as inconvenient discontinuities that make manipulation difficult. However, when these transitions are well-understood, they can be leveraged to reduce uncertainty or aid manipulation---for example, wiggling a screw to determine if it is fully inserted or not. Current model-free reinforcement learning approaches require large amounts of data to learn to leverage such dynamics, scale poorly as problem complexity grows, and do not transfer well to significantly different problems. By contrast, hierarchical planning-based methods scale well via plan decomposition and work well on a wide variety of problems, but often rely on precise hand-specified models and task decompositions. To combine the advantages of these opposing paradigms, we propose a new method, Act-CHAMP, which (1) learns hybrid kinematics models of objects from unsegmented data, (2) leverages actions, in addition to states, to outperform a state-of-the-art observation-only inference method, and (3) does so in a manner that is compatible with efficient, hierarchical POMDP planning. Beyond simply coping with challenging dynamics, we show that our end-to-end system leverages the learned kinematics to reduce uncertainty, plan efficiently, and use objects in novel ways not encountered during training.

Includes members:
  Member "category level articulated object pose estimation":
    also connected through other hyperedges:
      (none)
  Member "a review of robot learning for manipulation challenges representations and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "unsupervised discovery of parts structure and dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep variational luenberger type observer for stochastic video prediction")
  Member "inferring geometric constraints in human demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "maximum likelihood constraint inference for inverse reinforcement learning")
  Member "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation")
  Member "prediction and control with temporal segment models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sub goal trees a framework for goal based reinforcement learning")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "se3 nets learning rigid body motion using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sequential forecasting of 100 000 points")

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)