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 "on privacy amplification lossy compression and their duality to channel coding"):
  Abstract: We examine the task of privacy amplification from information-theoretic and coding-theoretic points of view. In the former, we give a one-shot characterization of the optimal rate of privacy amplification against classical adversaries in terms of the optimal type-II error in asymmetric hypothesis testing. The converse significantly improves on previous bounds based on smooth min-entropy by Watanabe and Hayashi  [7]  and turns out to be equivalent to a recent formulation in terms of the    $E_\gamma $    divergence by Yang  et al.   [9] . In the latter, we show that the protocols for privacy amplification based on linear codes can be easily repurposed for channel simulation. Combined with the known relations between channel simulation and lossy source coding, this implies that the privacy amplification can be understood as a basic primitive for both channel simulation and lossy compression. Applied to symmetric channels or lossy compression settings, our construction leads to protocols of the optimal rate in the asymptotic i.i.d. limit. Finally, appealing to the notion of channel duality recently detailed by us in  [15] , we show that the linear error-correcting codes for symmetric channels with quantum output can be transformed into linear lossy source coding schemes for classical variables arising from the dual channel. This explains a “curious duality” in these problems for the (self-dual) erasure channel observed by Martinian and Yedidia  [16]  and partly anticipates recent results on optimal lossy compression by polar and low-density generator matrix codes.

Includes members:
  Member "wiretap channels nonasymptotic fundamental limits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resource allocation for secure multi user downlink miso urllc systems")
  Member "uniform random number generation from markov chains non asymptotic and asymptotic analyses":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions")
  Member "alignment of polarized sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the fractality of polar and reed muller codes")
  Member "e_ gamma resolvability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on data processing and majorization inequalities for f divergences with applications")
  Member "approaching the rate distortion limit with spatial coupling belief propagation and decimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse regression codes")
  Member "non asymptotic output statistics of random binning and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "physical layer security in the finite blocklength regime over fading channels")
  Member "tight exponential analysis of universally composable privacy amplification and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication for generating correlation a unifying survey")
  Member "non asymptotic analysis of privacy amplification via renyi entropy and inf spectral entropy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two way physical layer security protocol for gaussian channels")

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)