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: "no internal regret via neighborhood watch"
  Abstract: We present an algorithm which attains O(\sqrt{T}) internal (and thus external) regret for finite games with partial monitoring under the local observability condition. Recently, this condition has been shown by (Bartok, Pal, and Szepesvari, 2011) to imply the O(\sqrt{T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured that the same holds for non-stochastic adversaries. Our result is in the affirmative, and it completes the characterization of possible rates for finite partial-monitoring games, an open question stated by (Cesa-Bianchi, Lugosi, and Stoltz, 2006). Our regret guarantees also hold for the more general model of partial monitoring with random signals.

Connected through hyperedge HE-1 (induced by source paper "exploration by optimisation in partial monitoring"):
  with members:
    - "on first order bounds variance and gap dependent bounds for adversarial bandits"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "connections between mirror descent thompson sampling and the information ratio"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "improved path length regret bounds for bandits"
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "more adaptive algorithms for adversarial bandits"

Connected through hyperedge HE-2 (induced by source paper "connections between mirror descent thompson sampling and the information ratio"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs without the graphs"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"

Connected through hyperedge HE-3 (induced by source paper "an information theoretic approach to minimax regret in partial monitoring"):
  with members:
    - "more adaptive algorithms for adversarial bandits"
    - "sparsity variance and curvature in multi armed bandits"
    - "online learning with feedback graphs beyond bandits"
    - "towards optimal algorithms for prediction with expert advice"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "online linear optimization via smoothing"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-4 (induced by source paper "cleaning up the neighborhood a full classification for adversarial partial monitoring"):
  with members:
    - "approachability of convex sets in games with partial monitoring"

Connected through hyperedge HE-5 (induced by source paper "online learning to rank with top k feedback"):
  with members:
    - "efficient learning in large scale combinatorial semi bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-6 (induced by source paper "utility based dueling bandits as a partial monitoring game"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-7 (induced by source paper "optimal resource allocation with semi bandit feedback"):
  with members:
    - "bandits with knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "regret analysis of stochastic and nonstochastic multi armed bandit problems"):
  with members:
    - "bandit algorithms for tree search"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "regret bounds for restless markov bandits"
    - "from bandits to experts on the value of side observations"
    - "efficient optimal learning for contextual bandits"
    - "toward a classification of finite partial monitoring games"
    - "on the universality of online mirror descent"

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)