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: "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
  Abstract: Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills. However, the iterative design process that is inevitable in practice is poorly supported by the default methodology. It is difficult to predict the outcomes of changes made to the reward functions, policy architectures, and the set of tasks being trained on. In this paper, we propose a practical method that allows the reward function to be fully redefined on each successive design iteration while limiting the deviation from the previous iteration. We characterize policies via sets of Deterministic Action Stochastic State (DASS) tuples, which represent the deterministic policy state-action pairs as sampled from the states visited by the trained stochastic policy. New policies are trained using a policy gradient algorithm which then mixes RL-based policy gradients with gradient updates defined by the DASS tuples. The tuples also allow for robust policy distillation to new network architectures. We demonstrate the effectiveness of this iterative-design approach on the bipedal robot Cassie, achieving stable walking with different gait styles at various speeds. We demonstrate the successful transfer of policies learned in simulation to the physical robot without any dynamics randomization, and that variable-speed walking policies for the physical robot can be represented by a small dataset of 5-10k tuples.

Connected through hyperedge HE-1 (induced by source paper "learning to manipulate object collections using grounded state representations"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "deep set prediction networks"
    - "learning agile and dynamic motor skills for legged robots"
    - "relational deep reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-2 (induced by source paper "reusable neural skill embeddings for vision guided whole body movement and object manipulation"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "robust recovery controller for a quadrupedal robot using deep reinforcement learning"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "learning agile and dynamic motor skills for legged robots"
    - "hierarchical visuomotor control of humanoids"
    - "deepmind control suite"

Connected through hyperedge HE-3 (induced by source paper "evolutionary reinforcement learning for sample efficient multiagent coordination"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "emergent coordination through competition"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "pommerman a multi agent playground"
    - "counterfactual multi agent policy gradients"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

Connected through hyperedge HE-4 (induced by source paper "gym ignition reproducible robotic simulations for reinforcement learning"):
  with members:
    - "modelling generalized forces with reinforcement learning for sim to real transfer"
    - "hierarchical reinforcement learning for quadruped locomotion"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "gym gazebo2 a toolkit for reinforcement learning using ros 2 and gazebo"
    - "learning agile and dynamic motor skills for legged robots"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-5 (induced by source paper "mpc net a first principles guided policy search"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "data efficient reinforcement learning for legged robots"
    - "on the convergence of adam and beyond"
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "an algorithmic perspective on imitation learning"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "bottom up meta policy search"):
  with members:
    - "learning humanoid robot motions through deep neural networks"
    - "on first order meta learning algorithms"
    - "evolved policy gradients"
    - "learning to learn meta critic networks for sample efficient learning"
    - "emergence of locomotion behaviours in rich environments"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "gait library synthesis for quadruped robots via augmented random search"):
  with members:
    - "hierarchical reinforcement learning for quadruped locomotion"
    - "design development and experimental realization of a quadrupedal research platform stoch"
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "sim to real learning agile locomotion for quadruped robots"
    - "realizing learned quadruped locomotion behaviors through kinematic motion primitives"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "learning active spine behaviors for dynamic and efficient locomotion in quadruped robots"):
  with members:
    - "design development and experimental realization of a quadrupedal research platform stoch"
    - "sim to real learning agile locomotion for quadruped robots"
    - "oncilla robot a versatile open source quadruped research robot with compliant pantograph legs"
    - "soft actor critic algorithms and applications"
    - "realizing learned quadruped locomotion behaviors through kinematic motion primitives"
    - "proximal policy optimization algorithms"
    - "tensorflow agents efficient batched reinforcement learning in tensorflow"
    - "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.RO(Robotics)