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: "cooperative inverse reinforcement learning"
  Abstract: For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL). A CIRL problem is a cooperative, partial-information game with two agents, human and robot; both are rewarded according to the human's reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.

Connected through hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "asking easy questions a user friendly approach to active reward learning"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "functional variational bayesian neural networks"
    - "causal confusion in imitation learning"
    - "a divergence minimization perspective on imitation learning methods"
    - "a simple baseline for bayesian uncertainty in deep learning"

Connected through hyperedge HE-2 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "autonomous interpretation of demonstrations for modification of dynamical movement primitives"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "including uncertainty when learning from human corrections"
    - "learning under misspecified objective spaces"
    - "probabilistically safe robot planning with confidence based human predictions"

Connected through hyperedge HE-3 (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-4 (induced by source paper "sequential cooperative bayesian inference"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "a mathematical theory of cooperative communication"
    - "generalizing the theory of cooperative inference"
    - "optimal cooperative inference"
    - "infant directed speech is consistent with teaching"
    - "machine teaching for bayesian learners in the exponential family"

Connected through hyperedge HE-5 (induced by source paper "steps towards value aligned systems"):
  with members:
    - "mimetic vs anchored value alignment in artificial intelligence"
    - "semantics derived automatically from language corpora contain human like biases"
    - "runaway feedback loops in predictive policing"

Connected through hyperedge HE-6 (induced by source paper "using human ratings for feedback control a supervised learning approach with application to rehabilitation robotics"):
  with members:
    - "constrained inverse optimal control with application to a human manipulation task"
    - "predictive modeling by infinite horizon constrained inverse optimal control with application to a human manipulation task"
    - "learning from richer human guidance augmenting comparison based learning with feature queries"

Connected through hyperedge HE-7 (induced by source paper "the incentives that shape behaviour"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-8 (induced by source paper "learning to interactively learn and assist"):
  with members:
    - "deep reinforcement learning from policy dependent human feedback"
    - "online meta learning"
    - "risk aware active inverse reinforcement learning"
    - "interactive learning with corrective feedback for policies based on deep neural networks"
    - "active inverse reward design"
    - "reward learning from human preferences and demonstrations in atari"
    - "task embedded control networks for few shot imitation learning"
    - "hg dagger interactive imitation learning with human experts"

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.AI(Artificial Intelligence)