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 "explicit bounds for entropy concentration under linear constraints"):
  Abstract: Consider the set of all sequences of    $n$    outcomes, each taking one of    $m$    values, whose frequency vectors satisfy a set of linear constraints. If    $m$    is fixed while    $n$    increases, most sequences that satisfy the constraints result in frequency vectors whose entropy approaches that of the maximum entropy vector satisfying the constraints. This well-known entropy concentration phenomenon underlies the maximum entropy method. Existing proofs of the concentration phenomenon are based on limits or asymptotics and unrealistically assume that constraints hold precisely, supporting maximum entropy inference more in principle than in practice. We present, for the first time, non-asymptotic, explicit lower bounds on    $n$    for a number of variants of the concentration result to hold to any prescribed accuracies, with the constraints holding up to any specified tolerance, considering the fact that allocations of discrete units can satisfy constraints only approximately. Again unlike earlier results, we measure concentration not by deviation from the maximum entropy value, but by the    $\ell _{1}$    and    $\ell _{2}$    distances from the maximum entropy-achieving frequency vector. One of our results holds independently of the alphabet size    $m$    and is based on a novel proof technique using the multi-dimensional Berry–Esseen theorem. We illustrate and compare our results using various detailed examples.

Includes members:
  Member "maximum entropy and the glasses you are looking through":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when ignorance is bliss")
  Member "entropy optimization and counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair distributions from biased samples a maximum entropy optimization framework")
  Member "analytical forms for most likely matrices derived from incomplete information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized entropy concentration for counts")
  Member "entropy concentration and the empirical coding game":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized entropy concentration for counts")

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.IT(Information Theory)