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: "heteroscedastic treed bayesian optimisation"
  Abstract: Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a Gaussian process prior that captures our belief about the distribution over functions. However, in many cases a single Gaussian process is not flexible enough to capture non-stationarity in the objective function. Consequently, heteroscedasticity negatively affects performance of traditional Bayesian methods. In this paper, we propose a novel prior model with hierarchical parameter learning that tackles the problem of non-stationarity in Bayesian optimisation. Our results demonstrate substantial improvements in a wide range of applications, including automatic machine learning and mining exploration.

Connected through hyperedge HE-1 (induced by source paper "robust policy search for robot navigation with stochastic meta policies"):
  with members:
    - "fully distributed bayesian optimization with stochastic policies"
    - "bayesian optimisation under uncertain inputs"
    - "using simulation to improve sample efficiency of bayesian optimization for bipedal robots"
    - "practical bayesian optimization in the presence of outliers"
    - "unscented bayesian optimization for safe robot grasping"
    - "funneled bayesian optimization for design tuning and control of autonomous systems"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "robots that can adapt like animals"

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"
    - "high dimensional bayesian optimization via additive models with overlapping groups"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "stochastic learning of nonstationary kernels for natural language modeling"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "learning to skim text"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "learning structural kernels for natural language processing"
    - "effective approaches to attention based neural machine translation"
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "task oriented learning of word embeddings for semantic relation classification"

Connected through hyperedge HE-4 (induced by source paper "kernelized hashcode representations for relation extraction"):
  with members:
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "deep variational information bottleneck"
    - "a survey on learning to hash"
    - "orthogonal random features"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "learning structural kernels for natural language processing"
    - "adaptive estimation of shannon entropy"

Connected through hyperedge HE-5 (induced by source paper "funneled bayesian optimization for design tuning and control of autonomous systems"):
  with members:
    - "robots that can adapt like animals"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "learning to optimize via posterior sampling"
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "advancing bayesian optimization the mixed global local mgl kernel and length scale cool down"):
  with members:
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

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)