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: "interactive teaching algorithms for inverse reinforcement learning"
  Abstract: We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic question: How could a teacher provide an informative sequence of demonstrations to an IRL learner to speed up the learning process? We present an interactive teaching framework where a teacher adaptively chooses the next demonstration based on learner's current policy. In particular, we design teaching algorithms for two concrete settings: an omniscient setting where a teacher has full knowledge about the learner's dynamics and a blackbox setting where the teacher has minimal knowledge. Then, we study a sequential variant of the popular MCE-IRL learner and prove convergence guarantees of our teaching algorithm in the omniscient setting. Extensive experiments with a car driving simulator environment show that the learning progress can be speeded up drastically as compared to an uninformative teacher.

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"
    - "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"
    - "teaching inverse reinforcement learners via features and demonstrations"

Connected through hyperedge HE-2 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints"):
  with members:
    - "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"
    - "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 "towards deployment of robust ai agents for human machine partnerships"):
  with members:
    - "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"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "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 "policy poisoning in batch reinforcement learning and control"):
  with members:
    - "data poisoning attacks on stochastic bandits"
    - "online data poisoning attack"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "data poisoning attacks against online learning"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-5 (induced by source paper "preference based batch and sequential teaching towards a unified view of models"):
  with members:
    - "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"
    - "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 "class teaching for inverse reinforcement learners"):
  with members:
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "teaching multiple concepts to forgetful learners"):
  with members:
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "the inaturalist species classification and detection dataset"
    - "string submodular functions with curvature constraints"
    - "unbounded human learning optimal scheduling for spaced repetition"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)