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: "adaptive ensemble learning with confidence bounds"
  Abstract: Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift. We illustrate the performance of Hedged Bandits in the context of medical informatics. However, the proposed methods have numerous other applications, including network monitoring and security, online recommendation systems, social networks, smart cities, etc.

Connected through hyperedge HE-1 (induced by source paper "exploiting relevance for online decision making in high dimensions"):
  with members:
    - "multi objective contextual multi armed bandit with a dominant objective"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "releaf an algorithm for learning and exploiting relevance"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "feature selection via regularized trees"
    - "efficient optimal learning for contextual bandits"
    - "x armed bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "budget constrained edge service provisioning with demand estimation via bandit learning"):
  with members:
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "spatio temporal edge service placement a bandit learning approach"
    - "a survey on mobile edge computing the communication perspective"
    - "computation peer offloading for energy constrained mobile edge computing in small cell networks"
    - "socially trusted collaborative edge computing in ultra dense networks"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "distributed online learning via cooperative contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "multi objective contextual multi armed bandit with a dominant objective"):
  with members:
    - "personalized course sequence recommendations"
    - "releaf an algorithm for learning and exploiting relevance"
    - "distributed online learning in social recommender systems"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "distributed online learning via cooperative contextual bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-4 (induced by source paper "tops ensemble learning with trees of predictors"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "ensemble of distributed learners for online classification of dynamic data streams"

Connected through hyperedge HE-5 (induced by source paper "fast and accurate inference with adaptive ensemble prediction in image classification with deep neural networks"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "popular ensemble methods an empirical study"

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)