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: "tuning modular networks with weighted losses for hand eye coordination"
  Abstract: This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently. Benefiting from weighted losses, the fine-tuning method significantly improves the performance of the policies for a robotic planar reaching task.

Connected through hyperedge HE-1 (induced by source paper "adversarial discriminative sim to real transfer of visuo motor policies"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "progressive neural networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "addressing appearance change in outdoor robotics with adversarial domain adaptation"
    - "bridging between computer and robot vision through data augmentation a case study on object recognition"

Connected through hyperedge HE-2 (induced by source paper "modular deep q networks for sim to real transfer of visuo motor policies"):
  with members:
    - "progressive neural networks"
    - "bridging between computer and robot vision through data augmentation a case study on object recognition"
    - "visual servoing from deep neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "towards vision based deep reinforcement learning for robotic motion control"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"
    - "adapting deep visuomotor representations with weak pairwise constraints"

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)