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 "barrier certificates for assured machine teaching"):
  Abstract: Machine teaching has received significant attention in the past few years as a paradigm shift from machine learning. While machine learning is often concerned with improving the performance of learners, machine teaching pertains to the efficiency of teachers. For example, machine teaching seeks to find the optimal (minimum) number of data samples needed for teaching a target hypothesis to a learner. Hence, it is natural to raise the question of how can we provide assurances for teaching given a machine teaching algorithm. In this paper, we address this question by borrowing notions from control theory. We begin by proposing a model based on partially observable Markov decision processes (POMDPs) for a class of machine teaching problems. We then show that the POMDP formulation can be cast as a special hybrid system, i.e., a discrete-time switched system. Subsequently, we use barrier certificates to verify properties of this special hybrid system. We show how the computation of the barrier certificate can be decomposed and numerically implemented as the solution to a sum-of-squares (SOS) program. For illustration, we show how the proposed framework based on control theory can be used to verify the teaching performance of two well-known machine teaching methods.

Includes members:
  Member "verification of uncertain pomdps using barrier certificates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "an optimal control approach to sequential machine teaching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nloptcontrol a modeling language for solving optimal control problems")
  Member "understanding the role of adaptivity in machine teaching the case of version space learners":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences")
  Member "privacy verification in pomdps via barrier certificates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "control theory meets pomdps a hybrid systems approach")
  Member "machine teaching a new paradigm for building machine learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "preference based teaching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences")

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)