human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neural replicator dynamics"):
  Abstract: Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarity and instability. In this paper, we first demonstrate that standard softmax-based policy gradient can be prone to poor performance in the presence of even the most benign nonstationarity. By contrast, it is known that the replicator dynamics, a well-studied model from evolutionary game theory, eliminates dominated strategies and exhibits convergence of the time-averaged trajectories to interior Nash equilibria in zero-sum games. Thus, using the replicator dynamics as a foundation, we derive an elegant one-line change to policy gradient methods that simply bypasses the gradient step through the softmax, yielding a new algorithm titled Neural Replicator Dynamics (NeuRD). NeuRD reduces to the exponential weights/Hedge algorithm in the single-state all-actions case. Additionally, NeuRD has formal equivalence to softmax counterfactual regret minimization, which guarantees convergence in the sequential tabular case. Importantly, our algorithm provides a straightforward way of extending the replicator dynamics to the function approximation setting. Empirical results show that NeuRD quickly adapts to nonstationarities, outperforming policy gradient significantly in both tabular and function approximation settings, when evaluated on the standard imperfect information benchmarks of Kuhn Poker, Leduc Poker, and Goofspiel.

Includes members:
  Member "the hanabi challenge a new frontier for ai research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "other play for zero shot coordination")
  Member "alpha rank multi agent evaluation by evolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "options as responses grounding behavioural hierarchies in multi agent rl")
  Member "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithms in multi agent systems a holistic perspective from reinforcement learning and game theory")
  Member "computing approximate equilibria in sequential adversarial games by exploitability descent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "qmix monotonic value function factorisation for deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "impala scalable distributed deep rl with importance weighted actor learner architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "revisiting cfr and alternating updates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to correlate in multi player general sum sequential games")
  Member "deep counterfactual regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithms in multi agent systems a holistic perspective from reinforcement learning and game theory")

Question: Please tell me which class the source paper 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)