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: "trading regret for efficiency online convex optimization with long term constraints"
  Abstract: In this paper we propose a framework for solving constrained online convex optimization problem. Our motivation stems from the observation that most algorithms proposed for online convex optimization require a projection onto the convex set $\mathcal{K}$ from which the decisions are made. While for simple shapes (e.g. Euclidean ball) the projection is straightforward, for arbitrary complex sets this is the main computational challenge and may be inefficient in practice. In this paper, we consider an alternative online convex optimization problem. Instead of requiring decisions belong to $\mathcal{K}$ for all rounds, we only require that the constraints which define the set $\mathcal{K}$ be satisfied in the long run. We show that our framework can be utilized to solve a relaxed version of online learning with side constraints addressed in \cite{DBLP:conf/colt/MannorT06} and \cite{DBLP:conf/aaai/KvetonYTM08}. By turning the problem into an online convex-concave optimization problem, we propose an efficient algorithm which achieves $\tilde{\mathcal{O}}(\sqrt{T})$ regret bound and $\tilde{\mathcal{O}}(T^{3/4})$ bound for the violation of constraints. Then we modify the algorithm in order to guarantee that the constraints are satisfied in the long run. This gain is achieved at the price of getting $\tilde{\mathcal{O}}(T^{3/4})$ regret bound. Our second algorithm is based on the Mirror Prox method \citep{nemirovski-2005-prox} to solve variational inequalities which achieves $\tilde{\mathcal{\mathcal{O}}}(T^{2/3})$ bound for both regret and the violation of constraints when the domain $\K$ can be described by a finite number of linear constraints. Finally, we extend the result to the setting where we only have partial access to the convex set $\mathcal{K}$ and propose a multipoint bandit feedback algorithm with the same bounds in expectation as our first algorithm.

Connected through hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-2 (induced by source paper "simple and fast algorithm for binary integer and online linear programming"):
  with members:
    - "online linear programming dual convergence new algorithms and regret bounds"
    - "how good is sgd with random shuffling"
    - "introduction to online convex optimization"
    - "online convex optimization for cumulative constraints"
    - "a linearly relaxed approximate linear program for markov decision processes"
    - "fast algorithms for online stochastic convex programming"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-3 (induced by source paper "bandit convex optimization for scalable and dynamic iot management"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "an online secretary framework for fog network formation with minimal latency"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "online learning for wireless distributed computing"
    - "online optimization competing with dynamic comparators"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"

Connected through hyperedge HE-4 (induced by source paper "distributed online optimization with long term constraints"):
  with members:
    - "introduction to online convex optimization"
    - "distributed online linear regression"
    - "online convex optimization for cumulative constraints"
    - "kernel based methods for bandit convex optimization"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "a stochastic view of optimal regret through minimax duality"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-5 (induced by source paper "distributed online linear regression"):
  with members:
    - "introduction to online convex optimization"
    - "online convex optimization for cumulative constraints"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"
    - "distributed submodular maximization"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-6 (induced by source paper "pricing mechanism for resource sustainability in competitive online learning multi agent systems"):
  with members:
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "learning in time varying games"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "on sample complexity of projection free primal dual methods for learning mixture policies in markov decision processes"):
  with members:
    - "scalable bilinear pi learning using state and action features"
    - "optimizing over a restricted policy class in markov decision processes"
    - "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"

Connected through hyperedge HE-8 (induced by source paper "semi decentralized coordinated online learning for continuous games with coupled constraints via augmented lagrangian"):
  with members:
    - "nash and wardrop equilibria in aggregative games with coupling constraints"
    - "online convex optimization and no regret learning algorithms guarantees and applications"

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)