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: "an adaptive clipping approach for proximal policy optimization"
  Abstract: Very recently proximal policy optimization (PPO) algorithms have been proposed as first-order optimization methods for effective reinforcement learning. While PPO is inspired by the same learning theory that justifies trust region policy optimization (TRPO), PPO substantially simplifies algorithm design and improves data efficiency by performing multiple epochs of \emph{clipped policy optimization} from sampled data. Although clipping in PPO stands for an important new mechanism for efficient and reliable policy update, it may fail to adaptively improve learning performance in accordance with the importance of each sampled state. To address this issue, a new surrogate learning objective featuring an adaptive clipping mechanism is proposed in this paper, enabling us to develop a new algorithm, known as PPO-$\lambda$. PPO-$\lambda$ optimizes policies repeatedly based on a theoretical target for adaptive policy improvement. Meanwhile, destructively large policy update can be effectively prevented through both clipping and adaptive control of a hyperparameter $\lambda$ in PPO-$\lambda$, ensuring high learning reliability. PPO-$\lambda$ enjoys the same simple and efficient design as PPO. Empirically on several Atari game playing tasks and benchmark control tasks, PPO-$\lambda$ also achieved clearly better performance than PPO.

Connected through hyperedge HE-1 (induced by source paper "off policy actor critic in an ensemble achieving maximum general entropy and effective environment exploration in deep reinforcement learning"):
  with members:
    - "effective exploration for deep reinforcement learning via bootstrapped q ensembles under tsallis entropy regularization"
    - "ucb exploration via q ensembles"
    - "sim to real learning agile locomotion for quadruped robots"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "trust pcl an off policy trust region method for continuous control"

Connected through hyperedge HE-2 (induced by source paper "effective exploration for deep reinforcement learning via bootstrapped q ensembles under tsallis entropy regularization"):
  with members:
    - "path consistency learning in tsallis entropy regularized mdps"
    - "equivalence between policy gradients and soft q learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "ucb and infogain exploration via boldsymbol q ensembles"
    - "bridging the gap between value and policy based reinforcement learning"
    - "sparse markov decision processes with causal sparse tsallis entropy regularization for reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "policy optimization with penalized point probability distance an alternative to proximal policy optimization"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "distributed prioritized experience replay"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "learning transferable architectures for scalable image recognition"
    - "a distributional perspective on 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.AI(Artificial Intelligence)