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: "context attentive bandits contextual bandit with restricted context"
  Abstract: We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling. Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context(TSRC) and the Windows Thompson Sampling with Restricted Context(WTSRC), for handling stationary and nonstationary environments, respectively. Our empirical results demonstrate advantages of the proposed approaches on several real-life datasets

Connected through hyperedge HE-1 (induced by source paper "survey bandits with regret guarantees"):
  with members:
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "how can ai automate end to end data science"):
  with members:
    - "automated machine learning via admm"
    - "meta learning a survey"
    - "incorporating behavioral constraints in online ai systems"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-3 (induced by source paper "a survey on practical applications of multi armed and contextual bandits"):
  with members:
    - "interpretable multi objective reinforcement learning through policy orchestration"
    - "low rank bandit methods for high dimensional dynamic pricing"
    - "bandit models of human behavior reward processing in mental disorders"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "building bridges viewing active learning from the multi armed bandit lens"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "algorithm selection as a bandit problem with unbounded losses"

Connected through hyperedge HE-4 (induced by source paper "a bandit approach to posterior dialog orchestration under a budget"):
  with members:
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "optimal exploitation of clustering and history information in multi armed bandit"):
  with members:
    - "interpretable multi objective reinforcement learning through policy orchestration"
    - "regional multi armed bandits"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "incorporating behavioral constraints in online ai systems"):
  with members:
    - "ai safety gridworlds"
    - "the social dilemma of autonomous vehicles"
    - "linear contextual bandits with knapsacks"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "playing atari with deep reinforcement learning"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"

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)