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: "teaching inverse reinforcement learners via features and demonstrations"
  Abstract: Learning near-optimal behaviour from an expert's demonstrations typically relies on the assumption that the learner knows the features that the true reward function depends on. In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i.e., where there is a mismatch between the worldviews of the learner and the expert. We introduce a natural quantity, the teaching risk, which measures the potential suboptimality of policies that look optimal to the learner in this setting. We show that bounds on the teaching risk guarantee that the learner is able to find a near-optimal policy using standard algorithms based on inverse reinforcement learning. Based on these findings, we suggest a teaching scheme in which the expert can decrease the teaching risk by updating the learner's worldview, and thus ultimately enable her to find a near-optimal policy.

Connected through hyperedge HE-1 (induced by source paper "learning to switch between machines and humans"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "regression under human assistance"
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "selectivenet a deep neural network with an integrated reject option"
    - "the algorithmic automation problem prediction triage and human effort"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "direct uncertainty prediction for medical second opinions"

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"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "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 "towards deployment of robust ai agents for human machine partnerships"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "learning to collaborate in markov decision processes"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning policy representations in multiagent systems"
    - "robust adversarial reinforcement learning"
    - "learning with opponent learning awareness"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-4 (induced by source paper "interactive teaching algorithms for inverse reinforcement learning"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "dual policy iteration"
    - "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-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"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "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:
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "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:
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "analyzing federated learning through an adversarial lens"
    - "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 "class teaching for inverse reinforcement learners"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "an overview of machine teaching"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "machine teaching for bayesian learners in the exponential family"
    - "multi armed bandits for intelligent tutoring systems"

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)