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: "bandit algorithms for tree search"
  Abstract: Bandit based methods for tree search have recently gained popularity when applied to huge trees, e.g. in the game of go [6]. Their efficient exploration of the tree enables to re- turn rapidly a good value, and improve preci- sion if more time is provided. The UCT algo- rithm [8], a tree search method based on Up- per Confidence Bounds (UCB) [2], is believed to adapt locally to the effective smoothness of the tree. However, we show that UCT is "over-optimistic" in some sense, leading to a worst-case regret that may be very poor. We propose alternative bandit algorithms for tree search. First, a modification of UCT us- ing a confidence sequence that scales expo- nentially in the horizon depth is analyzed. We then consider Flat-UCB performed on the leaves and provide a finite regret bound with high probability. Then, we introduce and analyze a Bandit Algorithm for Smooth Trees (BAST) which takes into account ac- tual smoothness of the rewards for perform- ing efficient "cuts" of sub-optimal branches with high confidence. Finally, we present an incremental tree expansion which applies when the full tree is too big (possibly in- finite) to be entirely represented and show that with high probability, only the optimal branches are indefinitely developed. We illus- trate these methods on a global optimization problem of a continuous function, given noisy values.

Connected through hyperedge HE-1 (induced by source paper "bayesian optimization for backpropagation in monte carlo tree search"):
  with members:
    - "bayesian optimization in alphago"

Connected through hyperedge HE-2 (induced by source paper "skinnerdb regret bounded query evaluation via reinforcement learning"):
  with members:
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "sampling based query re optimization"
    - "ordering selection operators using the minmax regret rule"
    - "simple regret optimization in online planning for markov decision processes"
    - "robust estimation of resource consumption for sql queries using statistical techniques"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-3 (induced by source paper "derivative free order robust optimisation"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "introduction to online convex optimization"
    - "a simple parameter free and adaptive approach to optimization under a minimal local smoothness assumption"
    - "overcoming catastrophic forgetting in neural networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "x armed bandits"

Connected through hyperedge HE-4 (induced by source paper "automated machine learning with monte carlo tree search"):
  with members:
    - "autostacker a compositional evolutionary learning system"
    - "benchmarking automatic machine learning frameworks"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "group equivariant convolutional networks"
    - "openml networked science in machine learning"
    - "x armed bandits"

Connected through hyperedge HE-5 (induced by source paper "practical open loop optimistic planning"):
  with members:
    - "approximate robust control of uncertain dynamical systems"

Connected through hyperedge HE-6 (induced by source paper "action selection for mdps anytime ao vs uct"):
  with members:
    - "anytime heuristic search"

Connected through hyperedge HE-7 (induced by source paper "optimal continuous state pomdp planning with semantic observations a variational approach"):
  with members:
    - "closed loop bayesian semantic data fusion for collaborative human autonomy target search"
    - "near optimal belief space planning via t lqg"
    - "deep value of information estimators for collaborative human machine information gathering"
    - "approximate safety verification and control of partially observable stochastic hybrid systems"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-8 (induced by source paper "a simple parameter free and adaptive approach to optimization under a minimal local smoothness assumption"):
  with members:
    - "follow the leader if you can hedge if you must"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "the best of both worlds stochastic and adversarial bandits"
    - "multi armed bandits in metric spaces"

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)