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: "achieving synergy in cognitive behavior of humanoids via deep learning of dynamic visuo motor attentional coordination"
  Abstract: The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN). The proposed model is built on coupling of a dynamic vision network, a motor generation network, and a higher level network allocated on top of these two. The simulation experiments using the iCub simulator were conducted for cognitive tasks including visual object manipulation responding to human gestures. The results showed that "synergetic" coordination can be developed via iterative learning through the whole network when spatio-temporal hierarchy and temporal one can be self-organized in the visual pathway and in the motor pathway, respectively, such that the higher level can manipulate them with abstraction.

Connected through hyperedge HE-1 (induced by source paper "deep learning in robotics a review of recent research"):
  with members:
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep exploration via bootstrapped dqn"
    - "brain4cars car that knows before you do via sensory fusion deep learning architecture"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "vime variational information maximizing exploration"
    - "deep learning in neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-2 (induced by source paper "seamless integration and coordination of cognitive skills in humanoid robots a deep learning approach"):
  with members:
    - "deep learning in neural networks"
    - "symbol emergence in robotics a survey"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"
    - "deep learning for detecting robotic grasps"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "predictive coding based deep dynamic neural network for visuomotor learning"):
  with members:
    - "seamless integration and coordination of cognitive skills in humanoid robots a deep learning approach"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic 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.AI(Artificial Intelligence)