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: "rao blackwellised particle filtering for dynamic bayesian networks"
  Abstract: Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.

Connected through hyperedge HE-1 (induced by source paper "poserbpf a rao blackwellized particle filter for 6d object pose tracking"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "focal loss for dense object detection"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "6 dof object pose from semantic keypoints"

Connected through hyperedge HE-2 (induced by source paper "language guided semantic mapping and mobile manipulation in partially observable environments"):
  with members:
    - "inferring compact representations for efficient natural language understanding of robot instructions"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "yolov3 an incremental improvement"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "learning models for following natural language directions in unknown environments"

Connected through hyperedge HE-3 (induced by source paper "factored probabilistic belief tracking"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "tractable inference for complex stochastic processes"
    - "context specific independence in bayesian networks"
    - "a general non probabilistic theory of inductive reasoning"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "a simple insight into iterative belief propagation s success"

Connected through hyperedge HE-4 (induced by source paper "online sampling from log concave distributions"):
  with members:
    - "introduction to online convex optimization"
    - "simulated tempering langevin monte carlo ii an improved proof using soft markov chain decomposition"
    - "logistic regression the importance of being improper"
    - "a tutorial on thompson sampling"
    - "learning without concentration"
    - "logistic regression tight bounds for stochastic and online optimization"

Connected through hyperedge HE-5 (induced by source paper "collapsed amortized variational inference for switching nonlinear dynamical systems"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "filtering variational objectives"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "reactive probabilistic programming"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "design and implementation of probabilistic programming language anglican"
    - "swift compiled inference for probabilistic programming languages"
    - "c3 lightweight incrementalized mcmc for probabilistic programs using continuations and callsite caching"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-7 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks extended abstract"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "factored probabilistic belief tracking"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"

Connected through hyperedge HE-8 (induced by source paper "causal belief decomposition for planning with sensing completeness results and practical approximation"):
  with members:
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "a knowledge compilation map"

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.LG(Machine Learning)