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 hierarchical control for robust in hand manipulation"):
  Abstract: Robotic in-hand manipulation has been a long-standing challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipulation sequences. To address these challenges, the majority of prior work has either focused on model-based, low-level controllers or on model-free deep reinforcement learning that each have their own limitations. We propose a hierarchical method that relies on traditional, model-based controllers on the low-level and learned policies on the mid-level. The low-level controllers can robustly execute different manipulation primitives (reposing, sliding, flipping). The mid-level policy orchestrates these primitives. We extensively evaluate our approach in simulation with a 3-fingered hand that controls three degrees of freedom of elongated objects. We show that our approach can move objects between almost all the possible poses in the workspace while keeping them firmly grasped. We also show that our approach is robust to inaccuracies in the object models and to observation noise. Finally, we show how our approach generalizes to objects of other shapes.

Includes members:
  Member "dual arm in hand manipulation and regrasping using dexterous manipulation graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "autonomous bimanual functional regrasping of novel object class instances")
  Member "residual policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "brpo batch residual policy optimization")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "never give up learning directed exploration strategies")
  Member "dexterous manipulation with deep reinforcement learning efficient general and low cost":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation")
  Member "residual reinforcement learning for robot control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning contact rich manipulation tasks with rigid position controlled robots learning to force control")
  Member "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking in hand manipulation")
  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 "real time robust finger gaits planning under object shape and dynamics uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "real time grasp planning for multi fingered hands by finger splitting")

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)