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: "a deep reinforcement learning approach for dynamically stable inverse kinematics of humanoid robots"
  Abstract: Real time calculation of inverse kinematics (IK) with dynamically stable configuration is of high necessity in humanoid robots as they are highly susceptible to lose balance. This paper proposes a methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using Deep Reinforcement Learning (RL). Our approach is based on the idea of exploring the entire configuration space of the robot and learning the best possible solutions using Deep Deterministic Policy Gradient (DDPG). The proposed strategy was evaluated on the highly articulated upper body of a humanoid model with 27 degree of freedom (DoF). The trained model was able to solve inverse kinematics for the end effectors with 90% accuracy while maintaining the balance in double support phase.

Connected through hyperedge HE-1 (induced by source paper "comparison of distal teacher learning with numerical and analytical methods to solve inverse kinematics for rigid body mechanisms"):
  with members:
    - "a comparison of policy search in joint space and cartesian space for refinement of skills"

Connected through hyperedge HE-2 (induced by source paper "parameter sharing reinforcement learning architecture for multi agent driving behaviors"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "parameter sharing deep deterministic policy gradient for cooperative multi agent reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "asynchronous methods for deep reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "learning to drive using inverse reinforcement learning and deep q networks"
    - "a centralized reinforcement learning method for multi agent job scheduling in grid"
    - "trust region policy optimization"

Connected through hyperedge HE-3 (induced by source paper "digrad multi task reinforcement learning with shared actions"):
  with members:
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "learning shared representations in multi task reinforcement learning"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-4 (induced by source paper "learning coordinated tasks using reinforcement learning in humanoids"):
  with members:
    - "digrad multi task reinforcement learning with shared actions"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "continuous control with deep reinforcement learning"

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)