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 "grasping and manipulation with a multi fingered hand"):
  Abstract: This thesis is concerned with deriving planning algorithms for robot manipulators. Manipulation has two effects, the robot has a physical effect on the object, and it also acquires information about the object. This thesis presents algorithms that treat both problems. First, I present an extension of the well-known piano mover's problem where a robot pushing an object must plan its movements as well as those of the object. This requires simultaneous planning in the joint space of the robot and the configuration space of the object, in contrast to the original problem which only requires planning in the latter space. The effects of a robot action on the object configuration are determined by the non-invertible rigid body mechanics. Second, I consider planning under uncertainty and in particular planning for information effects. I consider the case where a robot has to reach and grasp an object under pose uncertainty caused by shape incompleteness. The approach presented in this report is to study and possibly extend a new approach to artificial intelligence (A.I.) which has emerged in the last years in response to the necessity of building intelligent controllers for agents operating in unstructured stochastic environments. Such agents require the ability to learn by interaction with its environment an optimal action-selection behaviour. The main issue is that real-world problems are usually dynamic and unpredictable. Thus, the agent needs to update constantly its current image of the world using its sensors, which provide only a noisy description of the surrounding environment. Although there are different schools of thinking, with their own set of techniques, a brand new direction which unifies many A.I. researches is to formalise such agent/environment interactions as embedded systems with stochastic dynamics.

Includes members:
  Member "let s push things forward a survey on robot pushing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accurate vision based manipulation through contact reasoning")
  Member "multisensory learning framework for robot drumming":
    also connected through other hyperedges:
      (none)
  Member "robust and fast generation of top and side grasps for unknown objects":
    also connected through other hyperedges:
      (none)
  Member "metrics and benchmarks for remote shared controllers in industrial applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aging touch systematic and unbiased presentation of tactile stimuli")
  Member "automatic detection of myocontrol failures based upon situational context information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aging touch systematic and unbiased presentation of tactile stimuli")
  Member "hypothesis based belief planning for dexterous grasping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aging touch systematic and unbiased presentation of tactile stimuli")
  Member "feature based transfer learning for robotic push manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "let s push things forward a survey on robot pushing")
  Member "online planning algorithms for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")

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)