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 algorithm networks for composable robot learning"
  Abstract: This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is trained end-to-end from data. DAN combines the strengths of model-driven modular system design and data-driven end-to-end learning. The algorithms and models act as structural assumptions to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms, in order to achieve the best overall system performance. We illustrate the DAN methodology through a case study on a simulated robot system, which learns to navigate in complex 3-D environments with only local visual observations and an image of a partially correct 2-D floor map.

Connected through hyperedge HE-1 (induced by source paper "learning task driven control policies via information bottlenecks"):
  with members:
    - "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"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "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 "learning navigation costs from demonstration in partially observable environments"):
  with members:
    - "path integral networks end to end differentiable optimal control"
    - "memory augmented control networks"
    - "qmdp net deep learning for planning under partial observability"
    - "reinforcement learning via recurrent convolutional neural networks"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "value iteration networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "hybrid system identification using switching density networks"):
  with members:
    - "learning programmatically structured representations with perceptor gradients"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "verifiable reinforcement learning via policy extraction"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "dynamic routing between capsules"
    - "stochastic neural networks for hierarchical reinforcement learning"

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)