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: "understanding the role of adaptivity in machine teaching the case of version space learners"
  Abstract: In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teaching focuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner's new state. We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as "worst-case" (the learner picks the next hypothesis randomly from the version space) and "preference-based" (the learner picks hypothesis according to some global preference). Inspired by human teaching, we propose a new model where the learner picks hypotheses according to some local preference defined by the current hypothesis. We show that our model exhibits several desirable properties, e.g., adaptivity plays a key role, and the learner's transitions over hypotheses are smooth/interpretable. We develop efficient teaching algorithms and demonstrate our results via simulation and user studies.

Connected through hyperedge HE-1 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "teaching categories to human learners with visual explanations"
    - "learning linear temporal properties"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "learning task specifications from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "teaching categories to human learners with visual explanations"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "interactive teaching algorithms for inverse reinforcement learning"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "dual policy iteration"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "repeated inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "barrier certificates for assured machine teaching"
    - "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-5 (induced by source paper "preference based batch and sequential teaching towards a unified view of models"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "optimal collusion free teaching"
    - "an optimal control approach to sequential machine teaching"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an optimal control view of adversarial machine learning"
    - "quadratic upper bound for recursive teaching dimension of finite vc classes"
    - "near optimally teaching the crowd to classify"

Connected through hyperedge HE-6 (induced by source paper "collaborative and privacy preserving machine teaching via consensus optimization"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "data poisoning attacks in contextual bandits"
    - "the teaching dimension of linear learners"
    - "communication efficient distributed dual coordinate ascent"
    - "machine teaching for bayesian learners in the exponential family"
    - "neos server 4 0 administrative guide"

Connected through hyperedge HE-7 (induced by source paper "robust federated training via collaborative machine teaching using trusted instances"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "training set debugging using trusted items"
    - "the teaching dimension of linear learners"
    - "machine teaching for bayesian learners in the exponential family"
    - "identifying mislabeled training data"

Connected through hyperedge HE-8 (induced by source paper "long term personalization of an in home socially assistive robot for children with autism spectrum disorders"):
  with members:
    - "embodiment in socially interactive robots"
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "active learning with statistical models"

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.LG(Machine Learning)