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 "adaptive teaching of temporal logic formulas to learners with preferences"):
  Abstract: Machine teaching is an algorithmic framework for teaching a target hypothesis via a sequence of examples or demonstrations. We investigate machine teaching for temporal logic formulas -- a novel and expressive hypothesis class amenable to time-related task specifications. In the context of teaching temporal logic formulas, an exhaustive search even for a myopic solution takes exponential time (with respect to the time span of the task). We propose an efficient approach for teaching parametric linear temporal logic formulas. Concretely, we derive a necessary condition for the minimal time length of a demonstration to eliminate a set of hypotheses. Utilizing this condition, we propose a myopic teaching algorithm by solving a sequence of integer programming problems. We further show that, under two notions of teaching complexity, the proposed algorithm has near-optimal performance. The results strictly generalize the previous results on teaching preference-based version space learners. We evaluate our algorithm extensively under a variety of learner types (i.e., learners with different preference models) and interactive protocols (e.g., batched and adaptive). The results show that the proposed algorithms can efficiently teach a given target temporal logic formula under various settings, and that there are significant gains of teaching efficacy when the teacher adapts to the learner's current hypotheses or uses oracles.

Includes members:
  Member "transfer of temporal logic formulas in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "understanding the role of adaptivity in machine teaching the case of version space learners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints")
  Member "information guided temporal logic inference with prior knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer of temporal logic formulas in reinforcement learning")
  Member "mining parametric temporal logic properties in model based design for cyber physical systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transfer of temporal logic formulas in reinforcement learning")
  Member "census signal temporal logic inference for multiagent group behavior analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transfer of temporal logic formulas in reinforcement learning")
  Member "teaching categories to human learners with visual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "counterfactual visual explanations")
  Member "learning linear temporal properties":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning interpretable models in the property specification language")
  Member "machine teaching for inverse reinforcement learning algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations")

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)