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: "high dimensional bayesian optimization via additive models with overlapping groups"
  Abstract: Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring, and more. One of the most important challenges in BO is the development of algorithms that scale to high dimensions, which remains a key open problem despite recent progress. In this paper, we consider the approach of Kandasamy et al. (2015), in which the high-dimensional function decomposes as a sum of lower-dimensional functions on subsets of the underlying variables. In particular, we significantly generalize this approach by lifting the assumption that the subsets are disjoint, and consider additive models with arbitrary overlap among the subsets. By representing the dependencies via a graph, we deduce an efficient message passing algorithm for optimizing the acquisition function. In addition, we provide an algorithm for learning the graph from samples based on Gibbs sampling. We empirically demonstrate the effectiveness of our methods on both synthetic and real-world data.

Connected through hyperedge HE-1 (induced by source paper "mixed variable bayesian optimization"):
  with members:
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "hyperparameter optimization a spectral approach"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "importance weighted autoencoders"
    - "adam a method for stochastic optimization"
    - "analysis of boolean functions"

Connected through hyperedge HE-2 (induced by source paper "scalable global optimization via local bayesian optimization"):
  with members:
    - "scaling gaussian process regression with derivatives"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "a tutorial on thompson sampling"
    - "advancing bayesian optimization the mixed global local mgl kernel and length scale cool down"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "heteroscedastic treed bayesian optimisation"

Connected through hyperedge HE-3 (induced by source paper "bayesian optimization under heavy tailed payoffs"):
  with members:
    - "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs"
    - "on kernelized multi armed bandits"
    - "pac bayesian inequalities for martingales"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-4 (induced by source paper "high dimensional bayesian optimization via supervised dimension reduction"):
  with members:
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-5 (induced by source paper "a survey on policy search algorithms for learning robot controllers in a handful of trials"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "an algorithmic perspective on imitation learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "world models"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"

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)