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 "norm conflict resolution in stochastic domains"):
  Abstract: Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach to managing conflicting norms, which are common in human social interactions. Existing logic-based approaches suffer from normative explosion and are typically designed for deterministic environments; reward-based approaches lack principled ways of determining which normative alternatives exist in a given environment. We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity. We provide a proof-of-concept implementation in a simulated vacuum cleaning domain.

Includes members:
  Member "interpretable apprenticeship learning with temporal logic specifications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "elaborating on learned demonstrations with temporal logic specifications")
  Member "robust satisfaction of temporal logic specifications via reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time series learning using monotonic logical properties")
  Member "qualitative analysis of pomdps with temporal logic specifications for robotics applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "mdp optimal control under temporal logic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "planning with state abstractions for non markovian task specifications")
  Member "on graphical modeling of preference and importance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "encoding monotonic multi set preferences using ci nets preliminary report")

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.SY(Systems and Control)