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 "morphology agnostic visual robotic control"):
  Abstract: Existing approaches for visuomotor robotic control typically require characterizing the robot in advance by calibrating the camera or performing system identification. We propose MAVRIC, an approach that works with minimal prior knowledge of the robot's morphology, and requires only a camera view containing the robot and its environment and an unknown control interface. MAVRIC revolves around a mutual information-based method for self-recognition, which discovers visual "control points" on the robot body within a few seconds of exploratory interaction, and these control points in turn are then used for visual servoing. MAVRIC can control robots with imprecise actuation, no proprioceptive feedback, unknown morphologies including novel tools, unknown camera poses, and even unsteady handheld cameras. We demonstrate our method on visually-guided 3D point reaching, trajectory following, and robot-to-robot imitation.

Includes members:
  Member "replab a reproducible low cost arm benchmark platform for robotic learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rigid soft interactive learning for robust grasping")
  Member "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "contingency aware exploration in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "towards a generic diver following algorithm balancing robustness and efficiency in deep visual detection":
    also connected through other hyperedges:
      (none)
  Member "learning visual servoing with deep features and fitted q iteration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "acceleration of actor critic deep reinforcement learning for visual grasping in clutter by state representation learning based on disentanglement of a raw input image")
  Member "self supervised visual planning with temporal skip connections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "underwater multi robot convoying using visual tracking by detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "machine vision for improved human robot cooperation in adverse underwater conditions")
  Member "learning a visuomotor controller for real world robotic grasping using easily simulated depth images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain independent unsupervised learning to grasp the novel objects")

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)