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 "preference based batch and sequential teaching towards a unified view of models"):
  Abstract: Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest to lower teaching complexity and to achieve more natural teacher-learner interactions, several teaching models and complexity measures have been proposed for both the batch settings (e.g., worst-case, recursive, preference-based, and non-clashing models) as well as the sequential settings (e.g., local preference-based model). To better understand the connections between these different batch and sequential models, we develop a novel framework which captures the teaching process via preference functions $\Sigma$. In our framework, each function $\sigma \in \Sigma$ induces a teacher-learner pair with teaching complexity as $\TD(\sigma)$. We show that the above-mentioned teaching models are equivalent to specific types/families of preference functions in our framework. This equivalence, in turn, allows us to study the differences between two important teaching models, namely $\sigma$ functions inducing the strongest batch (i.e., non-clashing) model and $\sigma$ functions inducing a weak sequential (i.e., local preference-based) model. Finally, we identify preference functions inducing a novel family of sequential models with teaching complexity linear in the VC dimension of the hypothesis class: this is in contrast to the best known complexity result for the batch models which is quadratic in the VC dimension.

Includes members:
  Member "interactive teaching algorithms for inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to switch between machines and humans")
  Member "learner aware teaching inverse reinforcement learning with preferences and constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to switch between machines and humans")
  Member "optimal collusion free teaching":
    also connected through other hyperedges:
      (none)
  Member "an optimal control approach to sequential machine teaching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences")
  Member "teaching inverse reinforcement learners via features and demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to switch between machines and humans")
  Member "an optimal control view of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online data poisoning attack")
  Member "quadratic upper bound for recursive teaching dimension of finite vc classes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal collusion free teaching")

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.LG(Machine Learning)