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: "sequential probability assignment with binary alphabets and large classes of experts"
  Abstract: We analyze the problem of sequential probability assignment for binary outcomes with side information and logarithmic loss, where regret—or, redundancy—is measured with respect to a (possibly infinite) class of experts. We provide upper and lower bounds for minimax regret in terms of sequential complexities of the class, introduced in [14, 13]. These complexities were recently shown to give matching (up to logarithmic factors) upper and lower bounds for sequential prediction with general convex Lipschitz loss functions [11, 12]. To deal with unbounded gradients of the logarithmic loss, we present a new analysis that employs a sequential chaining technique with a Bernstein-typebound. The introduced complexities are intrinsic to the problem of sequential probability assignment, as illustrated by our lower bound. We also consider an example of a large class of experts parametrized by vectors in a high-dimensional Euclidean ball (or a Hilbert ball). The typical discretization approach fails, while our techniques give a non-trivial bound. For this problem we also present an algorithm based on regularization with a self-concordant barrier. This algorithm is of an independent interest, as it requires a bound on the function values rather than gradients.

Connected through hyperedge HE-1 (induced by source paper "logistic regression the importance of being improper"):
  with members:
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "fast rates in statistical and online learning"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-2 (induced by source paper "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity"):
  with members:
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates in statistical and online learning"

Connected through hyperedge HE-3 (induced by source paper "generalization of erm in stochastic convex optimization the dimension strikes back"):
  with members:
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "generalization in adaptive data analysis and holdout reuse"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

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.IT(Information Theory)