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 "deep reinforcement learning with explicitly represented knowledge and variable state and action spaces"):
  Abstract: We focus on a class of real-world domains, where gathering hierarchical knowledge is required to accomplish a task. Many problems can be represented in this manner, such as network penetration testing, targeted advertising or medical diagnosis. In our formalization, the task is to sequentially request pieces of information about a sample to build the knowledge hierarchy and terminate when suitable. Any of the learned pieces of information can be further analyzed, resulting in a complex and variable action space. We present a combination of techniques in which the knowledge hierarchy is explicitly represented and given to a deep reinforcement learning algorithm as its input. To process the hierarchical input, we employ Hierarchical Multiple-Instance Learning and to cope with the complex action space, we factor it with hierarchical softmax. Our end-to-end differentiable model is trained with A2C, a standard deep reinforcement learning algorithm. We demonstrate the method in a set of seven classification domains, where the task is to achieve the best accuracy with a set budget on the amount of information retrieved. Compared to baseline algorithms, our method achieves not only better results, but also better generalization.

Includes members:
  Member "effective medical test suggestions using deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning")
  Member "classification with costly features as a sequential decision making problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning")
  Member "discretizing continuous action space for on policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "particle based adaptive discretization for continuous control using deep reinforcement learning")
  Member "graph neural networks a review of methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "relational deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "discrete sequential prediction of continuous actions for deep rl":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "particle based adaptive discretization for continuous control using deep reinforcement learning")
  Member "efficient entropy for policy gradient with multidimensional action space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning")
  Member "approximation capability of neural networks on spaces of probability measures and tree structured domains":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning")

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)