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: "deterministic implementations for reproducibility in deep reinforcement learning"
  Abstract: While deep reinforcement learning (DRL) has led to numerous successes in recent years, reproducing these successes can be extremely challenging. One reproducibility challenge particularly relevant to DRL is nondeterminism in the training process, which can substantially affect the results. Motivated by this challenge, we study the positive impacts of deterministic implementations in eliminating nondeterminism in training. To do so, we consider the particular case of the deep Q-learning algorithm, for which we produce a deterministic implementation by identifying and controlling all sources of nondeterminism in the training process. One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance. We find that individual sources of nondeterminism can substantially impact the performance of agent, illustrating the benefits of deterministic implementations. In addition, we also discuss the important role of deterministic implementations in achieving exact replicability of results.

Connected through hyperedge HE-1 (induced by source paper "catalyst rl a distributed framework for reproducible rl research"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "latent space policies for hierarchical reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-2 (induced by source paper "the scientific method in the science of machine learning"):
  with members:
    - "model cards for model reporting"
    - "avoiding a tragedy of the commons in the peer review process"
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "natural environment benchmarks for reinforcement learning"
    - "datasheets for datasets"
    - "distilling information from a flood a possibility for the use of meta analysis and systematic review in machine learning research"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"

Connected through hyperedge HE-3 (induced by source paper "machine learning for combinatorial optimization a methodological tour d horizon"):
  with members:
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "predicting tactical solutions to operational planning problems under imperfect information"
    - "learning to optimize neural nets"
    - "automated treatment planning in radiation therapy using generative adversarial networks"
    - "learning a sat solver from single bit supervision"
    - "generative adversarial networks an overview"
    - "learning permutations with sinkhorn policy gradient"
    - "predicting solution summaries to integer linear programs under imperfect information with machine 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)