human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "human visual understanding for cognition and manipulation a primer for the roboticist"):
  Abstract: Robotic research is often built on approaches that are motivated by insights from self-examination of how we interface with the world. However, given current theories about human cognition and sensory processing, it is reasonable to assume that the internal workings of the brain are separate from how we interface with the world and ourselves. To amend some of these misconceptions arising from self-examination this article reviews human visual understanding for cognition and action, specifically manipulation. Our focus is on identifying overarching principles such as the separation into visual processing for action and cognition, hierarchical processing of visual input, and the contextual and anticipatory nature of visual processing for action. We also provide a rudimentary exposition of previous theories about visual understanding that shows how self-examination can lead down the wrong path. Our hope is that the article will provide insights for the robotic researcher that can help them navigate the path of self-examination, give them an overview of current theories about human visual processing, as well as provide a source for further relevant reading.

Includes members:
  Member "global search with bernoulli alternation kernel for task oriented grasping informed by simulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "interactive perception leveraging action in perception and perception in action":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action for better prediction")
  Member "deep learning a grasp function for grasping under gripper pose uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "learning hand eye coordination for robotic grasping with deep learning and large scale data collection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "learning to poke by poking experiential learning of intuitive physics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "the curious robot learning visual representations via physical interactions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "watching the world go by representation learning from unlabeled videos")
  Member "deep learning for tactile understanding from visual and haptic data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal deep learning for robot touch")
  Member "real time grasp detection using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")

Question: Please tell me which class the source paper 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)