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 "bridging commonsense reasoning and probabilistic planning via a probabilistic action language"):
  Abstract: To be responsive to dynamically changing real-world environments, an intelligent agent needs to perform complex sequential decision-making tasks that are often guided by commonsense knowledge. The previous work on this line of research led to the framework called "interleaved commonsense reasoning and probabilistic planning" (icorpp), which used P-log for representing commmonsense knowledge and Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of icorpp is that its implementation requires non-trivial engineering efforts to bridge the commonsense reasoning and probabilistic planning formalisms. In this paper, we present a unified framework to integrate icorpp's reasoning and planning components. In particular, we extend probabilistic action language pBC+ to express utility, belief states, and observation as in POMDP models. Inheriting the advantages of action languages, the new action language provides an elaboration tolerant representation of POMDP that reflects commonsense knowledge. The idea led to the design of the system pbcplus2pomdp, which compiles a pBC+ action description into a POMDP model that can be directly processed by off-the-shelf POMDP solvers to compute an optimal policy of the pBC+ action description. Our experiments show that it retains the advantages of icorpp while avoiding the manual efforts in bridging the commonsense reasoner and the probabilistic planner.

Includes members:
  Member "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty")
  Member "robot representing and reasoning with knowledge from reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty")
  Member "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "a probabilistic extension of action language bc":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "strong equivalence for lpmln programs")
  Member "integrating human provided information into belief state representation using dynamic factorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty")
  Member "integrating task motion planning with reinforcement learning for robust decision making in mobile robots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "desiderata for planning systems in general purpose service robots")
  Member "computing lpmln using asp and mln solvers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strong equivalence for lpmln programs")
  Member "probabilistic reasoning with answer sets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")

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)