human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "gp sum gaussian processes filtering of non gaussian beliefs"
  Abstract: This work centers on the problem of stochastic filtering for systems that yield complex beliefs. The main contribution is GP-SUM, a filtering algorithm for dynamic systems expressed as Gaussian Processes (GP), that does not rely on linearizations or Gaussian approximations of the belief. The algorithm can be seen as a combination of a sampling-based filter and a probabilistic Bayes filter. GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. Both, the sampling of the state and its propagation, are made possible by relying on the GP form of the system. In practice, the belief has the form of a weighted sum of Gaussians. We evaluate the performance of the algorithm with favorable comparisons against multiple versions of GP-Bayes filters on a standard synthetic problem. We also illustrate its practical use in a pushing task, and demonstrate that GP-SUM can predict heteroscedasticity, i.e., different amounts of uncertainty, and multi-modality when naturally occurring in pushing.

Connected through hyperedge HE-1 (induced by source paper "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"):
  with members:
    - "meta reinforcement learning as task inference"
    - "attentive neural processes"
    - "conditional neural processes"
    - "stochastic adversarial video prediction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "the perception distortion tradeoff"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-2 (induced by source paper "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "combining learned and analytical models for predicting action effects"
    - "stochastic variational video prediction"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "learning data efficient rigid body contact models case study of planar impact"
    - "a probabilistic data driven model for planar pushing"
    - "experimental validation of contact dynamics for in hand manipulation"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Question: Please tell me which class the center node 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)