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: "characterizing concept drift"
  Abstract: Most machine learning models are static, but the world is dynamic, and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non-stationary distributions, or as it is commonly called concept drift. However, the key issue of characterizing the different types of drift that can occur has not previously been subjected to rigorous definition and analysis. In particular, while some qualitative drift categorizations have been proposed, few have been formally defined, and the quantitative descriptions required for precise and objective understanding of learner performance have not existed. We present the first comprehensive framework for quantitative analysis of drift. This supports the development of the first comprehensive set of formal definitions of types of concept drift. The formal definitions clarify ambiguities and identify gaps in previous definitions, giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift.

Connected through hyperedge HE-1 (induced by source paper "lunar cellular automata for drifting data streams"):
  with members:
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "spiking neural networks and online learning an overview and perspectives"
    - "evaluating and characterizing incremental learning from non stationary data"
    - "scikit multiflow a multi output streaming framework"
    - "a survey of cellular automata types dynamics non uniformity and applications"
    - "why should i trust you explaining the predictions of any classifier"
    - "swarms on continuous data"

Connected through hyperedge HE-2 (induced by source paper "performative prediction"):
  with members:
    - "optimal decision making under strategic behavior"
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-3 (induced by source paper "streaming active deep forest for evolving data stream classification"):
  with members:
    - "online deep learning learning deep neural networks on the fly"
    - "deep forest towards an alternative to deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "engineering problems in machine learning systems"):
  with members:
    - "improving transparency of deep neural inference process"
    - "towards dependability metrics for neural networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "runtime monitoring neuron activation patterns"
    - "quantitative projection coverage for testing ml enabled autonomous systems"
    - "towards proving the adversarial robustness of deep neural networks"
    - "maximum resilience of artificial neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-5 (induced by source paper "spiking neural networks and online learning an overview and perspectives"):
  with members:
    - "deep spiking neural network with spike count based learning rule"
    - "deep learning in spiking neural networks"
    - "online machine learning in big data streams"
    - "scikit multiflow a multi output streaming framework"
    - "a systematic study of online class imbalance learning with concept drift"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-6 (induced by source paper "effectiveness assessment of cyber physical systems"):
  with members:
    - "the mythos of model interpretability"
    - "qoe modelling measurement and prediction a review"
    - "on the difficulty of training recurrent neural networks"
    - "general combination rules for qualitative and quantitative beliefs"
    - "the application of the matrix calculus to belief functions"

Connected through hyperedge HE-7 (induced by source paper "contextual one class classification in data streams"):
  with members:
    - "the management of context sensitive features a review of strategies"

Connected through hyperedge HE-8 (induced by source paper "exploiting a stimuli encoding scheme of spiking neural networks for stream learning"):
  with members:
    - "scikit multiflow a multi output streaming framework"

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)