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 "universal polar codes"):
  Abstract: Polar codes, invented by Arikan in 2009, are known to achieve the capacity of any binary-input memoryless output-symmetric channel. One of the few drawbacks of the original polar code construction is that it is not universal. This means that the code has to be tailored to the channel if we want to transmit close to capacity. #R##N#We present two "polar-like" schemes which are capable of achieving the compound capacity of the whole class of binary-input memoryless output-symmetric channels with low complexity. #R##N#Roughly speaking, for the first scheme we stack up $N$ polar blocks of length $N$ on top of each other but shift them with respect to each other so that they form a "staircase." Coding then across the columns of this staircase with a standard Reed-Solomon code, we can achieve the compound capacity using a standard successive decoder to process the rows (the polar codes) and in addition a standard Reed-Solomon erasure decoder to process the columns. Compared to standard polar codes this scheme has essentially the same complexity per bit but a block length which is larger by a factor $O(N \log_2(N)/\epsilon)$, where $\epsilon$ is the gap to capacity. #R##N#For the second scheme we first show how to construct a true polar code which achieves the compound capacity for a finite number of channels. We achieve this by introducing special "polarization" steps which "align" the good indices for the various channels. We then show how to exploit the compactness of the space of binary-input memoryless output-symmetric channels to reduce the compound capacity problem for this class to a compound capacity problem for a finite set of channels. This scheme is similar in spirit to standard polar codes, but the price for universality is a considerably larger blocklength. #R##N#We close with what we consider to be some interesting open problems.

Includes members:
  Member "finite length scaling for polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reed muller codes theory and algorithms")
  Member "performance limits and practical decoding of interleaved reed solomon polar concatenated codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "threshold secure coding with shared key")
  Member "the least degraded and the least upgraded channel with respect to a channel family":
    also connected through other hyperedges:
      (none)
  Member "compound polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "design of polar codes for parallel channels with an average power constraint")
  Member "spatially coupled ensembles universally achieve capacity under belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "on the construction of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "how to construct polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "concatenated polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polar like codes and asymptotic tradeoff among block length code rate and error probability")

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)