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: "boolean function analysis meets stochastic optimization an approximation scheme for stochastic knapsack"
  Abstract: The stochastic knapsack problem is the stochastic variant of the classical knapsack problem in which the algorithm designer is given a a knapsack with a given capacity and a collection of items where each item is associated with a profit and a probability distribution on its size. The goal is to select a subset of items with maximum profit and violate the capacity constraint with probability at most $p$ (referred to as the overflow probability). While several approximation algorithms have been developed for this problem, most of these algorithms relax the capacity constraint of the knapsack. In this paper, we design efficient approximation schemes for this problem without relaxing the capacity constraint. #R##N#(i) Our first result is in the case when item sizes are Bernoulli random variables. In this case, we design a (nearly) fully polynomial time approximation scheme (FPTAS) which only relaxes the overflow probability. (ii) Our second result generalizes the first result to the case when all the item sizes are supported on a (common) set of constant size. (iii) Our third result is in the case when item sizes are so-called "hypercontractive" random variables i.e., random variables whose second and fourth moments are within constant factors of each other. In other words, the kurtosis of the random variable is upper bounded by a constant. #R##N#Crucially, all of our algorithms meet the capacity constraint exactly, a result which was previously known only when the item sizes were Poisson or Gaussian random variables. Our results rely on new connections between Boolean function analysis and stochastic optimization. We believe that these ideas and techniques may prove to be useful in other stochastic optimization problems as well.

Connected through hyperedge HE-1 (induced by source paper "junta correlation is testable"):
  with members:
    - "distribution free junta testing"
    - "near log convexity of measured heat in discrete time and consequences"
    - "settling the query complexity of non adaptive junta testing"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "analysis of boolean functions"

Connected through hyperedge HE-2 (induced by source paper "learning sums of independent random variables with sparse collective support"):
  with members:
    - "playing anonymous games using simple strategies"
    - "a size free clt for poisson multinomials and its applications"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "big data on the rise testing monotonicity of distributions"
    - "learning arbitrary statistical mixtures of discrete distributions"
    - "properly learning poisson binomial distributions in almost polynomial time"

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.DS(Data Structures and Algorithms)