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 "effective diversity in population based reinforcement learning"):
  Abstract: Maintaining a population of solutions has been shown to increase exploration in reinforcement learning, typically attributed to the greater diversity of behaviors considered. One such class of methods, novelty search, considers further boosting diversity across agents via a multi-objective optimization formulation. Despite the intuitive appeal, these mechanisms have several shortcomings. First, they make use of mean field updates, which induce cycling behaviors. Second, they often rely on handcrafted behavior characterizations, which require domain knowledge. Furthermore, boosting diversity often has a detrimental impact on optimizing already fruitful behaviors for rewards. Setting the relative importance of novelty- versus reward-factor is usually hardcoded or requires tedious tuning/annealing. In this paper, we introduce a novel measure of population-wide diversity, leveraging ideas from Determinantal Point Processes. We combine this in a principled fashion with the reward function to adapt to the degree of diversity during training, borrowing ideas from online learning. Combined with task-agnostic behavioral embeddings, we show this approach outperforms previous methods for multi-objective optimization, as well as vanilla algorithms solely optimizing for rewards.

Includes members:
  Member "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance":
    also connected through other hyperedges:
      (none)
  Member "collaborative evolutionary reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi path policy optimization")
  Member "provably robust blackbox optimization for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy")
  Member "behavior guided reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "evolutionary reinforcement learning for sample efficient multiagent coordination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a generalized training approach for multiagent learning")
  Member "adapting behaviour for learning progress":
    also connected through other hyperedges:
      (none)
  Member "attraction repulsion actor critic for continuous control reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably efficient reconstruction of policy networks")
  Member "a generalized framework for population based training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)