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: "differentiable particle filters end to end learning with algorithmic priors"
  Abstract: We present differentiable particle filters (DPFs): a differentiable implementation of the particle filter algorithm with learnable motion and measurement models. Since DPFs are end-to-end differentiable, we can efficiently train their models by optimizing end-to-end state estimation performance, rather than proxy objectives such as model accuracy. DPFs encode the structure of recursive state estimation with prediction and measurement update that operate on a probability distribution over states. This structure represents an algorithmic prior that improves learning performance in state estimation problems while enabling explainability of the learned model. Our experiments on simulated and real data show substantial benefits from end-to- end learning with algorithmic priors, e.g. reducing error rates by ~80%. Our experiments also show that, unlike long short-term memory networks, DPFs learn localization in a policy-agnostic way and thus greatly improve generalization. Source code is available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "learning task driven control policies via information bottlenecks"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "learning efficient representation for intrinsic motivation"
    - "on variational bounds of mutual information"
    - "learning representations in reinforcement learning an information bottleneck approach"
    - "unsupervised visuomotor control through distributional planning networks"
    - "the limits and potentials of deep learning for robotics"
    - "particle filter networks with application to visual localization"
    - "unifying map and landmark based representations for visual navigation"

Connected through hyperedge HE-2 (induced by source paper "glas global to local safe autonomy synthesis for multi robot motion planning with end to end learning"):
  with members:
    - "trajectory generation for multiagent point to point transitions via distributed model predictive control"
    - "learning safe unlabeled multi robot planning with motion constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "graph neural networks for decentralized multi robot path planning"
    - "graph policy gradients for large scale unlabeled motion planning with constraints"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-3 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "chasing ghosts instruction following as bayesian state tracking"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "semi parametric topological memory for navigation"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-5 (induced by source paper "benchmarking classic and learned navigation in complex 3d environments"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "group normalization"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "particle filter networks with application to visual localization"

Connected through hyperedge HE-6 (induced by source paper "scene memory transformer for embodied agents in long horizon tasks"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active neural localization"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "visual representations for semantic target driven navigation"

Connected through hyperedge HE-7 (induced by source paper "not only look but observe variational observation model of scene level 3d multi object understanding for probabilistic slam"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "toward predictive machine learning for active vision"
    - "a variational feature encoding method of 3d object for probabilistic semantic slam"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "image2mesh a learning framework for single image 3d reconstruction"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-8 (induced by source paper "multi person pose tracking using sequential monte carlo with probabilistic neural pose predictor"):
  with members:
    - "pose estimator and tracker using temporal flow maps for limbs"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "uncertainty aware imitation learning using kernelized movement primitives"
    - "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "a fusion approach for multi frame optical flow estimation"

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)