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 "incremental learning of discrete planning domains from continuous perceptions"):
  Abstract: We propose a framework for learning discrete deterministic planning domains. In this framework, an agent learns the domain by observing the action effects through continuous features that describe the state of the environment after the execution of each action. Besides, the agent learns its perception function, i.e., a probabilistic mapping between state variables and sensor data represented as a vector of continuous random variables called perception variables. We define an algorithm that updates the planning domain and the perception function by (i) introducing new states, either by extending the possible values of state variables, or by weakening their constraints; (ii) adapts the perception function to fit the observed data (iii) adapts the transition function on the basis of the executed actions and the effects observed via the perception function. The framework is able to deal with exogenous events that happen in the environment.

Includes members:
  Member "learning plannable representations with causal infogan":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "pomdp model learning for human robot collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vector autoregressive pomdp model learning and planning for human robot collaboration")
  Member "learning in pomdps with monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "integrating acting planning and learning in hierarchical operational models")
  Member "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "classical planning in deep latent space bridging the subsymbolic symbolic boundary":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "strips action discovery")
  Member "learning strips operators from noisy and incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strips action discovery")
  Member "learning symbolic models of stochastic domains":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pddlgym gym environments from pddl 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)