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: "a probabilistic framework for learning kinematic models of articulated objects"
  Abstract: Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.

Connected through hyperedge HE-1 (induced by source paper "learning hybrid object kinematics for efficient hierarchical planning under uncertainty"):
  with members:
    - "category level articulated object pose estimation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised discovery of parts structure and dynamics"
    - "inferring geometric constraints in human demonstrations"
    - "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "kinematic morphing networks for manipulation skill transfer"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "progressive neural networks"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "unsupervised learning of 3d structure from images"
    - "sim to real robot learning from pixels with progressive nets"

Connected through hyperedge HE-3 (induced by source paper "factored pose estimation of articulated objects using efficient nonparametric belief propagation"):
  with members:
    - "pull message passing for nonparametric belief propagation"
    - "sum sequential scene understanding and manipulation"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "semantic robot programming for goal directed manipulation in cluttered scenes"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "scene grammars factor graphs and belief propagation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-4 (induced by source paper "interactive perception leveraging action in perception and perception in action"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "the curious robot learning visual representations via physical interactions"
    - "revisiting active perception"
    - "learning articulated motions from visual demonstration"
    - "learning contact rich manipulation skills with guided policy search"

Connected through hyperedge HE-5 (induced by source paper "reconstructing articulated rigged models from rgb d videos"):
  with members:
    - "capturing hands in action using discriminative salient points and physics simulation"
    - "learning articulated motions from visual demonstration"

Connected through hyperedge HE-6 (induced by source paper "spatiotemporal articulated models for dynamic slam"):
  with members:
    - "learning articulated motions from visual demonstration"

Connected through hyperedge HE-7 (induced by source paper "articulated motion learning via visual and lingual signals"):
  with members:
    - "learning articulated motions from visual demonstration"
    - "unsupervised learning of video representations using lstms"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning models for following natural language directions in unknown environments"
    - "what s cookin interpreting cooking videos using text speech and vision"
    - "sequence to sequence video to text"

Connected through hyperedge HE-8 (induced by source paper "learning articulated motion models from visual and lingual signals"):
  with members:
    - "learning articulated motions from visual demonstration"
    - "learning models for following natural language directions in unknown environments"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "deep visual semantic alignments for generating image descriptions"
    - "show and tell a neural image caption generator"
    - "distributed representations of words and phrases and their compositionality"

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)