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 "hybrid system identification using switching density networks"):
  Abstract: Behaviour cloning is a commonly used strategy for imitation learning and can be extremely effective in constrained domains. However, in cases where the dynamics of an environment may be state dependent and varying, behaviour cloning places a burden on model capacity and the number of demonstrations required. This paper introduces switching density networks, which rely on a categorical reparametrisation for hybrid system identification. This results in a network comprising a classification layer that is followed by a regression layer. We use switching density networks to predict the parameters of hybrid control laws, which are toggled by a switching layer to produce different controller outputs, when conditioned on an input state. This work shows how switching density networks can be used for hybrid system identification in a variety of tasks, successfully identifying the key joint angle goals that make up manipulation tasks, while simultaneously learning image-based goal classifiers and regression networks that predict joint angles from images. We also show that they can cluster the phase space of an inverted pendulum, identifying the balance, spin and pump controllers required to solve this task. Switching density networks can be difficult to train, but we introduce a cross entropy regularisation loss that stabilises training.

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 "learning programmatically structured representations with perceptor gradients":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "physics as inverse graphics joint unsupervised learning of objects and physics from video")
  Member "from explanation to synthesis compositional program induction for learning from demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "composing diverse policies for temporally extended tasks")
  Member "reinforcement and imitation learning for diverse visuomotor skills":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "one shot imitation from observing humans via domain adaptive meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting meta learning as supervised learning")
  Member "verifiable reinforcement learning via policy extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "learning complex dexterous manipulation with deep reinforcement learning and demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lightweight residual densely connected convolutional neural network")

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)