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: "online object and task learning via human robot interaction"
  Abstract: This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new tools and motions are taught on the fly. The robotic system developed was one of the five finalists in the KUKA Innovation Award competition and demonstrated during the Hanover Messe 2018 in Germany. The main contributions of the system are a) a novel incremental object learning module - a deep learning based localization and recognition system - that allows a human to teach new objects to the robot, b) an intuitive user interface for specifying 3D motion task associated with the new object, c) a hybrid force-vision control module for performing compliant motion on an unstructured surface. This paper describes the implementation and integration of the main modules of the system and summarizes the lessons learned from the competition.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised few shot object segmentation using co attention with visual and semantic inputs"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "one shot object detection with co attention and co excitation"
    - "online object representations with contrastive learning"
    - "adaptive masked proxies for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "youtube vos a large scale video object segmentation benchmark"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "visual geometric skill inference by watching human demonstration"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (induced by source paper "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"):
  with members:
    - "few shot goal inference for visuomotor learning and planning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"

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)