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: "learning coverage functions and private release of marginals"
  Abstract: We study the problem of approximating and learning coverage functions. A function $c: 2^{[n]} \rightarrow \mathbf{R}^{+}$ is a coverage function, if there exists a universe $U$ with non-negative weights $w(u)$ for each $u \in U$ and subsets $A_1, A_2, \ldots, A_n$ of $U$ such that $c(S) = \sum_{u \in \cup_{i \in S} A_i} w(u)$. Alternatively, coverage functions can be described as non-negative linear combinations of monotone disjunctions. They are a natural subclass of submodular functions and arise in a number of applications. #R##N#We give an algorithm that for any $\gamma,\delta>0$, given random and uniform examples of an unknown coverage function $c$, finds a function $h$ that approximates $c$ within factor $1+\gamma$ on all but $\delta$-fraction of the points in time $poly(n,1/\gamma,1/\delta)$. This is the first fully-polynomial algorithm for learning an interesting class of functions in the demanding PMAC model of Balcan and Harvey (2011). Our algorithms are based on several new structural properties of coverage functions. Using the results in (Feldman and Kothari, 2014), we also show that coverage functions are learnable agnostically with excess $\ell_1$-error $\epsilon$ over all product and symmetric distributions in time $n^{\log(1/\epsilon)}$. In contrast, we show that, without assumptions on the distribution, learning coverage functions is at least as hard as learning polynomial-size disjoint DNF formulas, a class of functions for which the best known algorithm runs in time $2^{\tilde{O}(n^{1/3})}$ (Klivans and Servedio, 2004). #R##N#As an application of our learning results, we give simple differentially-private algorithms for releasing monotone conjunction counting queries with low average error. In particular, for any $k \leq n$, we obtain private release of $k$-way marginals with average error $\bar{\alpha}$ in time $n^{O(\log(1/\bar{\alpha}))}$.

Connected through hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-2 (induced by source paper "tight approximation bounds for maximum multi coverage"):
  with members:
    - "algorithmic aspects of optimal channel coding"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "limitations of randomized mechanisms for combinatorial auctions"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-3 (induced by source paper "how to use heuristics for differential privacy"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "model agnostic private learning via stability"
    - "property testing for differential privacy"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "online learning via differential privacy"
    - "adaptive learning with robust generalization guarantees"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-4 (induced by source paper "statistical cost sharing"):
  with members:
    - "the sample complexity of revenue maximization"
    - "learning cooperative games"
    - "the limitations of optimization from samples"
    - "sampling and representation complexity of revenue maximization"
    - "mechanism design for data science"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "intriguing properties of neural networks"
    - "optimal bounds on approximation of submodular and xos functions by juntas"

Connected through hyperedge HE-5 (induced by source paper "learning powers of poisson binomial distributions"):
  with members:
    - "on the structure covering and learning of poisson multinomial distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "learning in auctions regret is hard envy is easy"
    - "testing poisson binomial distributions"

Connected through hyperedge HE-6 (induced by source paper "submodular optimization under noise"):
  with members:
    - "when are welfare guarantees robust"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "budget feasible mechanisms for experimental design"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "the computational complexity of truthfulness in combinatorial auctions"
    - "symmetry and approximability of submodular maximization problems"

Connected through hyperedge HE-7 (induced by source paper "learning combinatorial functions from pairwise comparisons"):
  with members:
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "learning pseudo boolean k dnf and submodular functions"
    - "active ranking using pairwise comparisons"
    - "learning valuation functions"
    - "privately releasing conjunctions and the statistical query barrier"

Connected through hyperedge HE-8 (induced by source paper "testing submodularity and other properties of valuation functions"):
  with members:
    - "analysis of boolean functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "partially symmetric functions are efficiently isomorphism testable"
    - "learning valuation functions"
    - "is submodularity testable"
    - "combinatorial auctions with decreasing marginal utilities"

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)