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 "self supervised damage avoiding manipulation strategy optimization via mental simulation"):
  Abstract: Everyday robotics are challenged to deal with autonomous product handling in applications like logistics or retail, possibly causing damage on the items during manipulation. Traditionally, most approaches try to minimize physical interaction with goods. However, we propose to take into account any unintended motion of objects in the scene and to learn manipulation strategies in a self-supervised way which minimize the potential damage. The presented approach consists of a planning method that determines the optimal sequence to manipulate a number of objects in a scene with respect to possible damage by simulating interaction and hence anticipating scene dynamics. The planned manipulation sequences are taken as input to a machine learning process which generalizes to new, unseen scenes in the same application scenario. This learned manipulation strategy is continuously refined in a self-supervised optimization cycle dur- ing load-free times of the system. Such a simulation-in-the-loop setup is commonly known as mental simulation and allows for efficient, fully automatic generation of training data as opposed to classical supervised learning approaches. In parallel, the generated manipulation strategies can be deployed in near-real time in an anytime fashion. We evaluate our approach on one industrial scenario (autonomous container unloading) and one retail scenario (autonomous shelf replenishment).

Includes members:
  Member "physics based damage aware manipulation strategy planning using scene dynamics anticipation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised damage avoiding manipulation strategy optimization via mental simulation")
  Member "robotic playing for hierarchical complex skill learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised damage avoiding manipulation strategy optimization via mental simulation")

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)