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: "high dimensional continuous control using generalized advantage estimation"
  Abstract: Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks. The two main challenges are the large number of samples typically required, and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data. We address the first challenge by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias, with an exponentially-weighted estimator of the advantage function that is analogous to TD(lambda). We address the second challenge by using trust region optimization procedure for both the policy and the value function, which are represented by neural networks. #R##N#Our approach yields strong empirical results on highly challenging 3D locomotion tasks, learning running gaits for bipedal and quadrupedal simulated robots, and learning a policy for getting the biped to stand up from starting out lying on the ground. In contrast to a body of prior work that uses hand-crafted policy representations, our neural network policies map directly from raw kinematics to joint torques. Our algorithm is fully model-free, and the amount of simulated experience required for the learning tasks on 3D bipeds corresponds to 1-2 weeks of real time.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence for prosthetics challenge solutions"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning to run challenge synthesizing physiologically accurate motion using deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "distributed distributional deterministic policy gradients"

Connected through hyperedge HE-2 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adaptive stress testing for autonomous vehicles"
    - "a survey of deep learning applications to autonomous vehicle control"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "adversarially robust generalization requires more data"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "particle based adaptive discretization for continuous control using deep reinforcement learning"):
  with members:
    - "discretizing continuous action space for on policy optimization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sfv reinforcement learning of physical skills from videos"
    - "soft actor critic algorithms and applications"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "successor uncertainties exploration and uncertainty in temporal difference learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "the uncertainty bellman equation and exploration"

Connected through hyperedge HE-5 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma"):
  with members:
    - "optimizing non orthogonal multiple access in random access networks"
    - "deep reinforcement learning for minimizing age of information in uav assisted networks"
    - "a game theoretic approach for noma aloha"
    - "reward constrained policy optimization"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "risk sensitive reinforcement learning a constrained optimization viewpoint"
    - "addressing function approximation error in actor critic methods"
    - "accelerated primal dual policy optimization for safe reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "analyzing visual representations in embodied navigation tasks"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "the replica dataset a digital replica of indoor spaces"
    - "dd ppo learning near perfect pointgoal navigators from 2 5 billion frames"
    - "exploration by random network distillation"
    - "group normalization"
    - "on evaluation of embodied navigation agents"
    - "a study on overfitting in deep reinforcement learning"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-7 (induced by source paper "taylor expansion policy optimization"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "adaptive trade offs in off policy learning"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "policy optimization through approximated importance sampling"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "policy optimization via importance sampling"

Connected through hyperedge HE-8 (induced by source paper "cluster based social reinforcement learning"):
  with members:
    - "message dropout an efficient training method for multi agent deep reinforcement learning"
    - "emergent coordination through competition"
    - "personalization of health interventions using cluster based reinforcement learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "m 3 rl mind aware multi agent management 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.LG(Machine Learning)