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 "qualitative numeric planning reductions and complexity"):
  Abstract: Qualitative numerical planning is classical planning extended with non-negative real variables that can be increased or decreased "qualitatively", i.e., by positive random amounts. While deterministic planning with numerical variables is undecidable in general, qualitative numerical planning is decidable and provides a convenient abstract model for generalized planning. Qualitative numerical planning, introduced by Srivastava, Zilberstein, Immerman, and Geffner (2011), showed that solutions to qualitative numerical problems (QNPs) correspond to the strong cyclic solutions of an associated fully observable non-deterministic (FOND) problem that terminate. The approach leads to a generate-and-test algorithm for solving QNPs where solutions to a FOND problem are generated one by one and tested for termination. The computational shortcomings of this approach, however, are that it is not simple to amend FOND planners to generate all solutions, and that the number of solutions to check can be doubly exponential in the number of variables. In this work we address these limitations, while providing additional insights on QNPs. More precisely, we introduce two reductions, one from QNPs to FOND problems and the other from FOND problems to QNPs both of which do not involve termination tests. A result of these reductions is that QNPs are shown to have the same expressive power and the same complexity as FOND problems.

Includes members:
  Member "transfer of deep reactive policies for mdp planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized neural policies for relational mdps")
  Member "compact policies for fully observable non deterministic planning as sat":
    also connected through other hyperedges:
      (none)
  Member "features projections and representation change for generalized planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guarantees for sound abstractions for generalized planning extended paper")
  Member "learning features and abstract actions for computing generalized plans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning first order symbolic representations for planning from the structure of the state space")
  Member "learning generalized reactive policies using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "action schema networks generalised policies with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalized neural policies for relational mdps")
  Member "mazebase a sandbox for learning from games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "why build an assistant in minecraft")
  Member "first order decision diagrams for relational mdps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guarantees for sound abstractions for generalized planning extended paper")

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.AI(Artificial Intelligence)