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 "direct policy gradients direct optimization of policies in discrete action spaces"):
  Abstract: Direct optimization is an appealing approach to differentiating through discrete quantities. Rather than relying on REINFORCE or continuous relaxations of discrete structures, it uses optimization in discrete space to compute gradients through a discrete argmax operation. In this paper, we develop reinforcement learning algorithms that use direct optimization to compute gradients of the expected return in environments with discrete actions. We call the resulting algorithms "direct policy gradient" algorithms and investigate their properties, showing that there is a built-in variance reduction technique and that a parameter that was previously viewed as a numerical approximation can be interpreted as controlling risk sensitivity. We also tackle challenges in algorithm design, leveraging ideas from A$^\star$ Sampling to develop a practical algorithm. Empirically, we show that the algorithm performs well in illustrative domains, and that it can make use of domain knowledge about upper bounds on return-to-go to speed up training.

Includes members:
  Member "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "babyai first steps towards grounded language learning with a human in the loop":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "direct optimization through arg max for discrete variational auto encoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")
  Member "woulda coulda shoulda counterfactually guided policy search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resolving spurious correlations in causal models of environments via interventions")
  Member "variational bayesian reinforcement learning with regret bounds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "making sense of reinforcement learning and probabilistic inference")
  Member "particle value functions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational bayesian reinforcement learning with regret bounds")
  Member "guided policy search as approximate mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic experience replay")
  Member "the concrete distribution a continuous relaxation of discrete random variables":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")

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)