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: "learning articulated motions from visual demonstration"
  Abstract: Many functional elements of human homes and workplaces consist of rigid components which are connected through one or more sliding or rotating linkages. Examples include doors and drawers of cabinets and appliances; laptops; and swivel office chairs. A robotic mobile manipulator would benefit from the ability to acquire kinematic models of such objects from observation. This paper describes a method by which a robot can acquire an object model by capturing depth imagery of the object as a human moves it through its range of motion. We envision that in future, a machine newly introduced to an environment could be shown by its human user the articulated objects particular to that environment, inferring from these "visual demonstrations" enough information to actuate each object independently of the user. #R##N#Our method employs sparse (markerless) feature tracking, motion segmentation, component pose estimation, and articulation learning; it does not require prior object models. Using the method, a robot can observe an object being exercised, infer a kinematic model incorporating rigid, prismatic and revolute joints, then use the model to predict the object's motion from a novel vantage point. We evaluate the method's performance, and compare it to that of a previously published technique, for a variety of household objects.

Connected through hyperedge HE-1 (induced by source paper "deep part induction from articulated object pairs"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "motion based object segmentation based on dense rgb d scene flow"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "multiframe scene flow with piecewise rigid motion"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"):
  with members:
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "spatiotemporal articulated models for dynamic slam"
    - "transfer from simulation to real world through learning deep inverse dynamics model"
    - "deep kinematic pose regression"
    - "end to end training of deep visuomotor policies"
    - "3d shapenets a deep representation for volumetric shapes"
    - "imagenet large scale visual recognition challenge"

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 contact rich manipulation skills with guided policy search"
    - "faster r cnn towards real time object detection with region proposal networks"

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"
    - "a probabilistic framework for learning kinematic models of articulated objects"

Connected through hyperedge HE-6 (induced by source paper "spatiotemporal articulated models for dynamic slam"):
  with members:
    - "a probabilistic framework for learning kinematic models of articulated objects"

Connected through hyperedge HE-7 (induced by source paper "articulated motion learning via visual and lingual signals"):
  with members:
    - "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"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-8 (induced by source paper "learning articulated motion models from visual and lingual signals"):
  with members:
    - "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"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)