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: "accelerated methods for deep reinforcement learning"
  Abstract: Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs. We confirm that both policy gradient and Q-value learning algorithms can be adapted to learn using many parallel simulator instances. We further find it possible to train using batch sizes considerably larger than are standard, without negatively affecting sample complexity or final performance. We leverage these facts to build a unified framework for parallelization that dramatically hastens experiments in both classes of algorithm. All neural network computations use GPUs, accelerating both data collection and training. Our results include using an entire NVIDIA DGX-1 to learn successful strategies in Atari games in single-digit minutes, using both synchronous and asynchronous algorithms.

Connected through hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework"):
  with members:
    - "deep reinforcement learning for cyber security"
    - "pytorch an imperative style high performance deep learning library"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "large scale study of curiosity driven learning"
    - "relational recurrent neural networks"
    - "distributed prioritized experience replay"
    - "a multi objective deep reinforcement learning framework"

Connected through hyperedge HE-2 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "on the convergence proof of amsgrad and a new version"
    - "smoothing policies and safe policy gradients"
    - "finite sample analysis for sarsa with linear function approximation"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "neural temporal difference learning converges to global optima"

Connected through hyperedge HE-3 (induced by source paper "seed rl scalable and efficient deep rl with accelerated central inference"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "torchbeast a pytorch platform for distributed rl"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "discovery of useful questions as auxiliary tasks"
    - "google research football a novel reinforcement learning environment"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "cross view policy learning for street navigation"
    - "neural replicator dynamics"

Connected through hyperedge HE-4 (induced by source paper "decentralized distributed ppo solving pointgoal navigation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "on evaluation of embodied navigation agents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "rlpyt a research code base for deep reinforcement learning in pytorch"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "an empirical model of large batch training"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "soft actor critic algorithms and applications"
    - "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"
    - "implicit quantile networks for distributional reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "dd ppo learning near perfect pointgoal navigators from 2 5 billion frames"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "on evaluation of embodied navigation agents"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "continual reinforcement learning in 3d non stationary environments"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "online meta learning"
    - "model primitive hierarchical lifelong reinforcement learning"
    - "task free continual learning"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "continuous learning in single incremental task scenarios"
    - "state representation learning for control an overview"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "a study on overfitting in deep reinforcement learning"
    - "neural modular control for embodied question answering"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "deep reinforcement learning that matters"
    - "home a household multimodal environment"
    - "ray a distributed framework for emerging ai applications"

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)