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: "multi step reinforcement learning a unifying algorithm"
  Abstract: Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD($\lambda$) elegantly unifies one-step TD prediction with Monte Carlo methods through the use of eligibility traces and the trace-decay parameter $\lambda$. Currently, there are a multitude of algorithms that can be used to perform TD control, including Sarsa, $Q$-learning, and Expected Sarsa. These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance. Each of these algorithms is seemingly distinct, and no one dominates the others for all problems. In this paper, we study a new multi-step action-value algorithm called $Q(\sigma)$ which unifies and generalizes these existing algorithms, while subsuming them as special cases. A new parameter, $\sigma$, is introduced to allow the degree of sampling performed by the algorithm at each step during its backup to be continuously varied, with Sarsa existing at one extreme (full sampling), and Expected Sarsa existing at the other (pure expectation). $Q(\sigma)$ is generally applicable to both on- and off-policy learning, but in this work we focus on experiments in the on-policy case. Our results show that an intermediate value of $\sigma$, which results in a mixture of the existing algorithms, performs better than either extreme. The mixture can also be varied dynamically which can result in even greater performance.

Connected through hyperedge HE-1 (induced by source paper "combating the compounding error problem with a multi step model"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "model ensemble trust region policy optimization"
    - "model based active exploration"
    - "lipschitz continuity in model based reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-2 (induced by source paper "smix lambda enhancing centralized value functions for cooperative multi agent reinforcement learning"):
  with members:
    - "understanding multi step deep reinforcement learning a systematic study of the dqn target"
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-3 (induced by source paper "context aware active multi step reinforcement learning"):
  with members:
    - "understanding multi step deep reinforcement learning a systematic study of the dqn target"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to explore with meta policy gradient"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "exploration enhanced politex"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "deepmind control suite"
    - "large scale study of curiosity driven learning"
    - "model free linear quadratic control via reduction to expert prediction"
    - "distributed prioritized experience replay"
    - "is q learning provably efficient"
    - "prioritized experience replay"
    - "scale free algorithms for online linear optimization"

Connected through hyperedge HE-5 (induced by source paper "unifying ensemble methods for q learning via social choice theory"):
  with members:
    - "the condorcet principle for multiwinner elections from shortlisting to proportionality"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "epopt learning robust neural network policies using model ensembles"
    - "dueling network architectures for deep reinforcement learning"
    - "properties of multiwinner voting rules"
    - "off policy shaping ensembles in reinforcement learning"
    - "justified representation in approval based committee voting"

Connected through hyperedge HE-6 (induced by source paper "robust temporal difference learning for critical domains"):
  with members:
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "value decomposition networks for cooperative multi agent learning"
    - "the option critic architecture"
    - "safe and efficient off policy reinforcement learning"
    - "multidefender security games"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-7 (induced by source paper "tbq sigma improving efficiency of trace utilization for off policy reinforcement learning"):
  with members:
    - "qualitative measurements of policy discrepancy for return based deep q network"
    - "a unified approach for multi step temporal difference learning with eligibility traces in reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "evolution guided policy gradient in reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"

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)