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: "practical bayesian optimization in the presence of outliers"
  Abstract: Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference. This allows outstanding sample efficiency because the probabilistic machinery provides a memory of the whole optimization process. However, that virtue becomes a disadvantage when the memory is populated with outliers, inducing bias in the estimation. In this paper, we present an empirical evaluation of Bayesian optimization methods in the presence of outliers. The empirical evidence shows that Bayesian optimization with robust regression often produces suboptimal results. We then propose a new algorithm which combines robust regression (a Gaussian process with Student-t likelihood) with outlier diagnostics to classify data points as outliers or inliers. By using an scheduler for the classification of outliers, our method is more efficient and has better convergence over the standard robust regression. Furthermore, we show that even in controlled situations with no expected outliers, our method is able to produce better results.

Connected through hyperedge HE-1 (induced by source paper "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "robust optimization for non convex objectives"
    - "on kernelized multi armed bandits"

Connected through hyperedge HE-2 (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"
    - "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"
    - "heteroscedastic treed bayesian optimisation"

Connected through hyperedge HE-3 (induced by source paper "distantly supervised question parsing"):
  with members:
    - "introduction to neural network based approaches for question answering over knowledge graphs"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "building a conversational agent overnight with dialogue self play"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "learning to compose neural networks for question answering"
    - "learning to compose words into sentences with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "fully distributed bayesian optimization with stochastic policies"):
  with members:
    - "funneled bayesian optimization for design tuning and control of autonomous systems"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "active learning for autonomous intelligent agents exploration curiosity and interaction"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "orchestrate infrastructure for enabling parallelism during hyperparameter optimization"):
  with members:
    - "nsml a machine learning platform that enables you to focus on your models"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

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)