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: "bandit convex optimization for scalable and dynamic iot management"
  Abstract: This paper deals with online convex optimization involving both time-varying loss functions, and time-varying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (also known as bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing in the Internet-of-Things, where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of online bandit saddle-point (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: 1)  dynamic regret  that generalizes the widely used static regret and 2)  fit  that captures the accumulated amount of constraint violations. Specifically, BanSaP is proved to simultaneously yield sublinear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog computation offloading tasks corroborate that our proposed BanSaP approach offers competitive performance relative to existing approaches that are based on gradient feedback.

Connected through hyperedge HE-1 (induced by source paper "bandit convex optimization in non stationary environments"):
  with members:
    - "introduction to online convex optimization"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "dynamic regret of strongly adaptive methods"
    - "corralling a band of bandit algorithms"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "online optimization competing with dynamic comparators"

Connected through hyperedge HE-2 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-3 (induced by source paper "learning and management for internet of things accounting for adaptivity and scalability"):
  with members:
    - "introduction to online convex optimization"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "multi timescale online optimization of network function virtualization for service chaining"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "joint cache resource allocation and request routing for in network caching services"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"

Connected through hyperedge HE-4 (induced by source paper "secure mobile edge computing in iot via collaborative online learning"):
  with members:
    - "introduction to online convex optimization"
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "socially trusted collaborative edge computing in ultra dense networks"
    - "traffic aware patching for cyber security in mobile iot"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "risk averse multi armed bandit problems under mean variance measure"
    - "delay and cooperation in nonstochastic bandits"
    - "a survey on wireless security technical challenges recent advances and future trends"

Connected through hyperedge HE-5 (induced by source paper "zeroth order stochastic variance reduction for nonconvex optimization"):
  with members:
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "practical black box attacks against machine learning"
    - "zeroth order asynchronous doubly stochastic algorithm with variance reduction"
    - "towards evaluating the robustness of neural networks"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"
    - "on the complexity of bandit and derivative free stochastic convex optimization"

Connected through hyperedge HE-6 (induced by source paper "learn and pick right nodes to offload"):
  with members:
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "decentralized computation offloading game for mobile cloud computing"
    - "online learning under delayed feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "online optimal task offloading with one bit feedback"):
  with members:
    - "learn and pick right nodes to offload"
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "online stochastic linear optimization under one bit feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)