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: "re proving channel polarization theorems an extremality and robustness analysis"
  Abstract: Abstract The general subject considered in this thesis is a recently discovered coding technique,polar coding, which is used to construct a class of error correction codes withunique properties. In his ground-breaking work, Arikan proved that this class ofcodes, called polar codes, achieve the symmetric capacity — the mutual informationevaluated at the uniform input distribution — of any stationary binary discretememoryless channel with low complexity encoders and decoders requiring in theorder of O(NlogN) operations in the block-length N. This discovery settledthe long standing open problem left by Shannon of ﬁnding low complexity codesachieving the channel capacity.Polar codes are not only appealing for being the ﬁrst to ‘close the deal’. Incontrast to most of the existing coding schemes, polar codes admit an explicit lowcomplexity construction. In addition, for symmetric channels, the polar code con-struction is deterministic; the theoretically beautiful but practically limited “averageperformance of an ensemble of codes is good, so there must exist one particular codein the ensemble at least as good as the average” formalism of information theory isbypassed. Simulations are thus not necessary in principle for evaluating the errorprobability which is shown in a study by Telatar and Arikan to scale exponentially inthe square root of the block-length. As such, at the time of this writing, polar codesare appealing for being the only class of codes proved, and proved with mathematicalelegance, to possess all of these properties.Polar coding settled an open problem in information theory, yet opened plentyof challenging problems that need to be addressed. This novel coding scheme is apromising method from which, in addition to data transmission, problems such asdata compression or compressed sensing, which includes all types of measurementprocesses like the MRI or ultrasound, could beneﬁt in terms of efﬁciency. To makethis technique fulﬁll its promise, the original theory has been, and should still be,extended in multiple directions. A signiﬁcant part of this thesis is dedicated toadvancing the knowledge about this technique in two directions. The ﬁrst oneprovides a better understanding of polar coding by generalizing some of the existingresults and discussing their implications, and the second one studies the robustnessof the theory over communication models introducing various forms of uncertaintyor variations into the probabilistic model of the channel.The idea behind the design of a polar code is a phenomenon called channelxi

Connected through hyperedge HE-1 (induced by source paper "channel polarization through the lens of blackwell measures"):
  with members:
    - "general strong polarization"
    - "polar codes for broadcast channels"
    - "varentropy decreases under the polar transform"
    - "finite length scaling for polar codes"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "polar codes for the two user multiple access channel"
    - "on the construction of polar codes"
    - "polar codes for q ary channels q 2 r"

Connected through hyperedge HE-2 (induced by source paper "channel capacity of polar coding with a given polar mismatched successive cancellation decoder"):
  with members:
    - "a simple proof of fast polarization"
    - "on the scaling exponent of polar codes for binary input energy harvesting channels"
    - "a split reduced successive cancellation list decoder for polar codes"
    - "a low latency list successive cancellation decoding implementation for polar codes"
    - "a high throughput list decoder architecture for polar codes"
    - "fast list decoders for polar codes"
    - "symbol decision successive cancellation list decoder for polar codes"
    - "polar coding for processes with memory"

Connected through hyperedge HE-3 (induced by source paper "mismatched multi letter successive decoding for the multiple access channel"):
  with members:
    - "random coding error exponents for the two user interference channel"
    - "polarization as a novel architecture to boost the classical mismatched capacity of b dmcs"
    - "a technique for deriving one shot achievability results in network information theory"
    - "multiuser coding techniques for mismatched decoding"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a general formula for the mismatch capacity"
    - "on achievable rates for channels with mismatched decoding"
    - "multiuser random coding techniques for mismatched decoding"

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)