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: "the price of bandit information in multiclass online classification"
  Abstract: We consider two scenarios of multiclass online learning of a hypothesis class $H\subseteq Y^X$. In the {\em full information} scenario, the learner is exposed to instances together with their labels. In the {\em bandit} scenario, the true label is not exposed, but rather an indication whether the learner's prediction is correct or not. We show that the ratio between the error rates in the two scenarios is at most $8\cdot|Y|\cdot \log(|Y|)$ in the realizable case, and $\tilde{O}(\sqrt{|Y|})$ in the agnostic case. The results are tight up to a logarithmic factor and essentially answer an open question from (Daniely et. al. - Multiclass learnability and the erm principle). #R##N#We apply these results to the class of $\gamma$-margin multiclass linear classifiers in $\reals^d$. We show that the bandit error rate of this class is $\tilde{\Theta}(\frac{|Y|}{\gamma^2})$ in the realizable case and $\tilde{\Theta}(\frac{1}{\gamma}\sqrt{|Y|T})$ in the agnostic case. This resolves an open question from (Kakade et. al. - Efficient bandit algorithms for online multiclass prediction).

Connected through hyperedge HE-1 (induced by source paper "bandit multiclass linear classification efficient algorithms for the separable case"):
  with members:
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "logistic regression the importance of being improper"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "learning depth three neural networks in polynomial time"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "contextual bandits with surrogate losses margin bounds and efficient algorithms"):
  with members:
    - "practical contextual bandits with regression oracles"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "making contextual decisions with low technical debt"
    - "efficient algorithms for adversarial contextual learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "contextual bandit learning with predictable rewards"
    - "on the universality of online mirror descent"

Connected through hyperedge HE-3 (induced by source paper "multiclass learnability and the erm principle"):
  with members:
    - "error correcting tournaments"
    - "solving multiclass learning problems via error correcting output codes"

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)