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: "maximum a posteriori policy optimisation"
  Abstract: We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative entropy objective. We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are competitive with the state-of-the-art in deep reinforcement learning. In particular, for continuous control, our method outperforms existing methods with respect to sample efficiency, premature convergence and robustness to hyperparameter settings while achieving similar or better final performance.

Connected through hyperedge HE-1 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement"):
  with members:
    - "gradient surgery for multi task learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "learning to reach goals without reinforcement learning"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning latent plans from play"
    - "advantage weighted regression simple and scalable off policy reinforcement learning"
    - "deepmind control suite"

Connected through hyperedge HE-2 (induced by source paper "keep doing what worked behavioral modelling priors for offline reinforcement learning"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "way off policy batch deep reinforcement learning of implicit human preferences in dialog"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "off policy deep reinforcement learning without exploration"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-4 (induced by source paper "imagined value gradients model based policy optimization with transferable latent dynamics models"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "information asymmetry in kl regularized rl"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration via state marginal matching"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "robel robotics benchmarks for learning with low cost robots"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "concurrent meta reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "provably efficient maximum entropy exploration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "exploration by random network distillation"

Connected through hyperedge HE-6 (induced by source paper "stabilizing transformers for reinforcement learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "meta reinforcement learning as task inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-7 (induced by source paper "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning powerful policies by using consistent dynamics model"
    - "mega reward achieving human level play without extrinsic rewards"
    - "model based reinforcement learning for atari"
    - "strategic object oriented reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-8 (induced by source paper "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "off policy actor critic with shared experience replay"
    - "transformer xl attentive language models beyond a fixed length context"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "supervised policy update for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"

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)