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: "alignment of polarized sets"
  Abstract: Arkan’s polar coding technique is based on the idea of synthesizing   $n$   channels from the   $n$   instances of the physical channel by a simple linear encoding transformation. Each synthesized channel corresponds to a particular input to the encoder. For large   $n$  , the synthesized channels become either essentially noiseless or almost perfectly noisy, but in total carry as much information as the original   $n$   channels. Capacity can therefore be achieved by transmitting messages over the essentially noiseless synthesized channels. Unfortunately, the set of inputs corresponding to reliable synthesized channels is poorly understood, in particular, how the set depends on the underlying physical channel. In this work, we present two analytic conditions sufficient to determine if the reliable inputs corresponding to different discrete memoryless channels are  aligned  or not, i.e., if one set is contained in the other. Understanding the alignment of the polarized sets is important as it is directly related to universality properties of the induced polar codes, which are essential in particular for network coding problems. We demonstrate the performance of our conditions on a few examples for wiretap and broadcast channels. Finally, we show that these conditions imply that the simple quantum polar coding scheme of Renes  et al.  [ Phys. Rev. Lett.,  109, 050504, 2012] requires entanglement assistance for general channels, but also show such assistance to be unnecessary in many cases of interest.

Connected through hyperedge HE-1 (induced by source paper "the fractality of polar and reed muller codes"):
  with members:
    - "relaxed polar codes"
    - "algebraic properties of polar codes from a new polynomial formalism"
    - "high girth matrices and polarization"
    - "ergodic theory meets polarization ii a foundation of polarization theory"
    - "finite length scaling for polar codes"
    - "a novel partial order for the information sets of polar codes over b dmcs"
    - "polarization of the r enyi information dimension for single and multi terminal analog compression"
    - "how to construct polar codes"

Connected through hyperedge HE-2 (induced by source paper "on privacy amplification lossy compression and their duality to channel coding"):
  with members:
    - "wiretap channels nonasymptotic fundamental limits"
    - "uniform random number generation from markov chains non asymptotic and asymptotic analyses"
    - "e_ gamma resolvability"
    - "approaching the rate distortion limit with spatial coupling belief propagation and decimation"
    - "non asymptotic output statistics of random binning and its applications"
    - "tight exponential analysis of universally composable privacy amplification and its applications"
    - "non asymptotic analysis of privacy amplification via renyi entropy and inf spectral entropy"
    - "fixed length lossy compression in the finite blocklength regime"

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.IT(Information Theory)