human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "control theory meets pomdps a hybrid systems approach"):
  with members:
    - "safe policy synthesis in multi agent pomdps via discrete time barrier functions"
    - "verification of uncertain pomdps using barrier certificates"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "privacy verification in pomdps via barrier certificates"
    - "machine teaching a new paradigm for building machine learning systems"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "heuristic search value iteration for pomdps"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"

Connected through hyperedge HE-2 (induced by source paper "safe policy synthesis in multi agent pomdps via discrete time barrier functions"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "privacy verification in pomdps via barrier certificates"
    - "shielded decision making in mdps"
    - "enhancing the performance of a safe controller via supervised learning for truck lateral control"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-3 (induced by source paper "cost bounded active classification using partially observable markov decision processes"):
  with members:
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "privacy verification in pomdps via barrier certificates"
    - "pomdp model learning for human robot collaboration"
    - "synthesis of surveillance strategies via belief abstraction"
    - "active classification theory and application to underwater inspection"

Question: Please tell me which class the center node 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)