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: "multi armed bandits for intelligent tutoring systems"
  Abstract: We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. #R##N#The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children.

Connected through hyperedge HE-1 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-2 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"

Connected through hyperedge HE-3 (induced by source paper "teacher algorithms for curriculum learning of deep rl in continuously parameterized environments"):
  with members:
    - "reinforcement learning for improving agent design"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"

Connected through hyperedge HE-4 (induced by source paper "class teaching for inverse reinforcement learners"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "mutual reinforcement learning"):
  with members:
    - "the assistive multi armed bandit"
    - "learning to teach"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "analysis of the apparent nuclear modification in peripheral pb pb collisions at 5 02 tev"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "accuracy based curriculum learning in deep reinforcement learning"):
  with members:
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "teacher student curriculum learning"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "complexity reduction in the negotiation of new lexical conventions"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)