human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "maximum entropy distributions bit complexity and stability"):
  Abstract: Maximum entropy distributions with discrete support in $m$ dimensions arise in machine learning, statistics, information theory, and theoretical computer science. While structural and computational properties of max-entropy distributions have been extensively studied, basic questions such as: Do max-entropy distributions over a large support (e.g., $2^m$) with a specified marginal vector have succinct descriptions (polynomial-size in the input description)? and: Are entropy maximizing distributions "stable" under the perturbation of the marginal vector? have resisted a rigorous resolution. #R##N#Here we show that these questions are related and resolve both of them. Our main result shows a ${\rm poly}(m, \log 1/\varepsilon)$ bound on the bit complexity of $\varepsilon$-optimal dual solutions to the maximum entropy convex program -- for very general support sets and with no restriction on the marginal vector. Applications of this result include polynomial time algorithms to compute max-entropy distributions over several new and old polytopes for any marginal vector in a unified manner, a polynomial time algorithm to compute the Brascamp-Lieb constant in the rank-1 case. The proof of this result allows us to show that changing the marginal vector by $\delta$ changes the max-entropy distribution in the total variation distance roughly by a factor of ${\rm poly}(m, \log 1/\delta)\sqrt{\delta}$ -- even when the size of the support set is exponential. Together, our results put max-entropy distributions on a mathematically sound footing -- these distributions are robust and computationally feasible models for data.

Includes members:
  Member "fair and diverse dpp based data summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interventions for ranking in the presence of implicit bias")
  Member "a generalization of permanent inequalities and applications in counting and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gauges loops and polynomials for partition functions of graphical models")
  Member "much faster algorithms for matrix scaling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "flows in almost linear time via adaptive preconditioning")
  Member "matrix scaling and balancing via box constrained newton s method and interior point methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flows in almost linear time via adaptive preconditioning")
  Member "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rigorous guarantees for tyler s m estimator via quantum expansion")
  Member "on the complexity of constrained determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fair and diverse dpp based data summarization")
  Member "real stable polynomials and matroids optimization and counting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic sampling from graphical models")
  Member "entropy optimization and counting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fair distributions from biased samples a maximum entropy optimization framework")

Question: Please tell me which class the source paper 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)