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 "estimating mass distribution of articulated objects through physical interaction"):
  Abstract: We explore the problem of estimating the mass distribution of an articulated object by an interactive agent. Our method predicts the mass distribution accurately only using information that can be reliably acquired by the limited sensing and actuating capabilities of a robotic agent that interacts with it. Inspired by the role of exploratory play in human infants, we take the combined approach of supervised and reinforcement learning to train the agent such that it learns to strategically interact with the object for estimating its mass distribution. Our method consists of two neural networks: the policy network which decides how to interact with the object, and the predictor network that estimates the mass distribution given a history of observations and interactions. Using our method, we train a robotic arm to estimate the mass distribution of an object with moving parts (e.g. an articulated rigid body system) by pushing it on a surface with unknown friction properties. We also test the robustness of our learned model by transferring it to another robot arm with different end-effector geometry. The empirical results show that our method significantly outperforms the baseline agent which uses random pushes to collect data for estimation.

Includes members:
  Member "learning to singulate objects using a push proposal network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "environment probing interaction policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation")
  Member "bounce and learn modeling scene dynamics with real world bounces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation")
  Member "densephysnet learning dense physical object representations via multi step dynamic interactions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "learning manipulation under physics constraints with visual perception":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation")
  Member "learning kinematic descriptions using spare simulated and physical articulated extendable dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation")
  Member "unsupervised learning of latent physical properties using perception prediction networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "predicting the physical dynamics of unseen 3d objects")

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)