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 non prehensile manipulation"):
  Abstract: We explore the problem of estimating the mass distribution of an articulated object by an interactive robotic agent. Our method predicts the mass distribution of an object by using limited sensing and actuating capabilities of a robotic agent during an interaction with the object. Inspired by the role of exploratory play in human infants, we take the combined approach of supervised and reinforcement learning to train an agent such that it learns to strategically interact with the object for estimating its mass distribution. Our method consists of two neural networks: (i) the policy network which decides how to interact with the object, and (ii) 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 show the robustness of our method across different physics simulators and robotic platforms. We further test our method on a real robot platform with 3D printed articulated chains with varying mass distributions. We present results that demonstrate that our method significantly outperforms the baseline agent that uses random pushes to interact with the object.

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 "network randomization a simple technique for generalization in deep reinforcement learning")
  Member "bounce and learn modeling scene dynamics with real world bounces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural re simulation for generating bounces in single images")
  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 physical interaction")
  Member "single episode policy transfer in reinforcement learning":
    also connected through other hyperedges:
      (none)
  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 physical interaction")
  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")

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)