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 "online learning in planar pushing with combined prediction model"):
  Abstract: Pushing is a useful robotic capability for positioning and reorienting objects. The ability to accurately predict the effect of pushes can enable efficient trajectory planning and complicated object manipulation. Physical prediction models for planar pushing have long been established, but their assumptions and requirements usually don't hold in most practical settings. Data-driven approaches can provide accurate predictions for offline data, but they often have generalizability issues. In this paper, we propose a combined prediction model and an online learning framework for planar push prediction. The combined model consists of a neural network module and analytical components with a low-dimensional parameter. We train the neural network offline using pre-collected pushing data. In online situations, the low-dimensional analytical parameter is learned directly from online pushes to quickly adapt to the new environments. We test our combined model and learning framework on real pushing experiments. Our experimental results show that our model is able to quickly adapt to new environments while achieving similar final prediction performance as that of pure neural network models.

Includes members:
  Member "improvisation through physical understanding using novel objects as tools with visual foresight":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "goal conditioned end to end visuomotor control for versatile skill primitives")
  Member "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "deep online learning via meta learning continual adaptation for model based rl":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a neural dirichlet process mixture model for task free continual learning")
  Member "combining learned and analytical models for predicting action effects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video")
  Member "a probabilistic data driven model for planar pushing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "let s push things forward a survey on robot pushing")
  Member "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "let s push things forward a survey on robot pushing")
  Member "unsupervised learning for physical interaction through video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "se3 nets learning rigid body motion using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential forecasting of 100 000 points")

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)