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: "online convex optimization using predictions"
  Abstract: Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. We prove that achieving sublinear regret and constant competitive ratio for online algorithms requires the use of an unbounded prediction window in adversarial settings, but that under more realistic stochastic prediction error models it is possible to use Averaging Fixed Horizon Control (AFHC) to simultaneously achieve sublinear regret and constant competitive ratio in expectation using only a constant-sized prediction window. Furthermore, we show that the performance of AFHC is tightly concentrated around its mean.

Connected through hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory"):
  with members:
    - "chasing convex bodies optimally"
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "competitive online optimization under inventory constraints"
    - "online control with adversarial disturbances"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "online inventory management with application to energy procurement in data centers"

Connected through hyperedge HE-2 (induced by source paper "understand dynamic regret with switching cost for online decision making"):
  with members:
    - "introduction to online convex optimization"
    - "proximal online gradient is optimum for dynamic regret"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "metrical task systems on trees via mirror descent and unfair gluing"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"

Connected through hyperedge HE-3 (induced by source paper "the bayesian prophet a low regret framework for online decision making"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "online stochastic matching new algorithms and bounds"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "fast algorithms for online stochastic convex programming"
    - "bandits with knapsacks"
    - "primal beats dual on online packing lps in the random order model"

Connected through hyperedge HE-4 (induced by source paper "online optimization with predictions and non convex losses"):
  with members:
    - "chasing convex bodies optimally"
    - "introduction to online convex optimization"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "improved path length regret bounds for bandits"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "potential function proofs for first order methods"

Connected through hyperedge HE-5 (induced by source paper "competitive online optimization under inventory constraints"):
  with members:
    - "balancing cost and dissatisfaction in online ev charging under real time pricing"
    - "nested convex bodies are chaseable"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online offering strategies for storage assisted renewable power producer in hour ahead market"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "peak aware online economic dispatching for microgrids"
    - "the submodular secretary problem goes linear"
    - "online energy generation scheduling for microgrids with intermittent energy sources and co generation"

Connected through hyperedge HE-6 (induced by source paper "beyond online balanced descent an optimal algorithm for smoothed online optimization"):
  with members:
    - "introduction to online convex optimization"
    - "competitive ratio versus regret minimization achieving the best of both worlds"
    - "a nearly linear bound for chasing nested convex bodies"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "competitively chasing convex bodies"
    - "better bounds for online line chasing"
    - "potential function proofs for first order methods"
    - "nested convex bodies are chaseable"

Connected through hyperedge HE-7 (induced by source paper "learning assisted competitive algorithms for peak aware energy scheduling"):
  with members:
    - "optimal algorithms for ski rental with soft machine learned predictions"
    - "online inventory management with application to energy procurement in data centers"
    - "competitive caching with machine learned advice"
    - "peak aware online economic dispatching for microgrids"
    - "reducing electricity demand charge for data centers with partial execution"

Connected through hyperedge HE-8 (induced by source paper "smoothed online optimization for regression and control"):
  with members:
    - "introduction to online convex optimization"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"

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)