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: "information based complexity feedback and dynamics in convex programming"
  Abstract: We study the intrinsic limitations of sequential convex optimization through the lens of feedback information theory. In the oracle model of optimization, an algorithm queries an {\em oracle} for noisy information about the unknown objective function, and the goal is to (approximately) minimize every function in a given class using as few queries as possible. We show that, in order for a function to be optimized, the algorithm must be able to accumulate enough information about the objective. This, in turn, puts limits on the speed of optimization under specific assumptions on the oracle and the type of feedback. Our techniques are akin to the ones used in statistical literature to obtain minimax lower bounds on the risks of estimation procedures; the notable difference is that, unlike in the case of i.i.d. data, a sequential optimization algorithm can gather observations in a {\em controlled} manner, so that the amount of information at each step is allowed to change in time. In particular, we show that optimization algorithms often obey the law of diminishing returns: the signal-to-noise ratio drops as the optimization algorithm approaches the optimum. To underscore the generality of the tools, we use our approach to derive fundamental lower bounds for a certain active learning problem. Overall, the present work connects the intuitive notions of information in optimization, experimental design, estimation, and active learning to the quantitative notion of Shannon information.

Connected through hyperedge HE-1 (induced by source paper "on the complexity of minimizing convex finite sums without using the indices of the individual functions"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "tight regret bounds for noisy optimization of a brownian motion"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-3 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation"):
  with members:
    - "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "worst case vs average case design for estimation from fixed pairwise comparisons"
    - "an adaptive sublinear time block sparse fourier transform"
    - "lower bounds on active learning for graphical model selection"
    - "f divergence inequalities"
    - "on the difficulty of selecting ising models with approximate recovery"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"

Connected through hyperedge HE-4 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"):
  with members:
    - "iterate averaging as regularization for stochastic gradient descent"
    - "tight analyses for non smooth stochastic gradient descent"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-5 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure"):
  with members:
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "tight analyses for non smooth stochastic gradient descent"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"

Connected through hyperedge HE-6 (induced by source paper "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the importance of communities for learning to influence"
    - "an adaptivity hierarchy theorem for property testing"
    - "settling the query complexity of non adaptive junta testing"

Connected through hyperedge HE-7 (induced by source paper "semantics representations and grammars for deep learning"):
  with members:
    - "deep online convex optimization by putting forecaster to sleep"
    - "blocks and fuel frameworks for deep learning"
    - "deep learning in neural networks"
    - "fast convergence of regularized learning in games"
    - "continuous control with deep reinforcement learning"
    - "gradient estimation using stochastic computation graphs"
    - "compatible value gradients for reinforcement learning of continuous deep policies"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-8 (induced by source paper "statistical query algorithms for stochastic convex optimization"):
  with members:
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "sum of squares lower bounds for planted clique"
    - "preventing false discovery in interactive data analysis is hard"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "preserving statistical validity in adaptive data analysis"
    - "private multiplicative weights beyond linear queries"

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)