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: "computing maximum entropy distributions everywhere"
  Abstract: We study the problem of computing the maximum entropy distribution with a specified expectation over a large discrete domain. Maximum entropy distributions arise and have found numerous applications in economics, machine learning and various sub-disciplines of mathematics and computer science. The key computational questions related to maximum entropy distributions are whether they have succinct descriptions and whether they can be efficiently computed. Here we provide positive answers to both of these questions for very general domains and, importantly, with no restriction on the expectation. This completes the picture left open by the prior work on this problem which requires that the expectation vector is polynomially far in the interior of the convex hull of the domain. As a consequence we obtain a general algorithmic tool and show how it can be applied to derive several old and new results in a unified manner. In particular, our results imply that certain recent continuous optimization formulations, for instance, for discrete counting and optimization problems, the matrix scaling problem, and the worst case Brascamp-Lieb constants in the rank-1 regime, are efficiently computable. Attaining these implications requires reformulating the underlying problem as a version of maximum entropy computation where optimization also involves the expectation vector and, hence, cannot be assumed to be sufficiently deep in the interior. The key new technical ingredient in our work is a polynomial bound on the bit complexity of near-optimal dual solutions to the maximum entropy convex program. This result is obtained by a geometrical reasoning that involves convex analysis and polyhedral geometry, avoiding combinatorial arguments based on the specific structure of the domain. We also provide a lower bound on the bit complexity of near-optimal solutions showing the tightness of our results.

Connected through hyperedge HE-1 (induced by source paper "rigorous guarantees for tyler s m estimator via quantum expansion"):
  with members:
    - "spectral analysis of matrix scaling and operator scaling"
    - "operator scaling via geodesically convex optimization invariant theory and polynomial identity testing"
    - "efficient algorithms for tensor scaling quantum marginals and moment polytopes"
    - "alternating minimization scaling algorithms and the null cone problem from invariant theory"
    - "maximum entropy distributions bit complexity and stability"
    - "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"
    - "operator scaling theory and applications"
    - "a deterministic polynomial time algorithm for non commutative rational identity testing"

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)