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: "how to achieve the capacity of asymmetric channels"
  Abstract: We survey coding techniques that enable reliable transmission at rates that approach the capacity of an arbitrary discrete memoryless channel. In particular, we take the point of view of modern coding theory and discuss how recent advances in coding for symmetric channels help provide more efficient solutions for the asymmetric case. We consider, in more detail, three basic coding paradigms. #R##N#The first one is Gallager's scheme that consists of concatenating a linear code with a non-linear mapping so that the input distribution can be appropriately shaped. We explicitly show that both polar codes and spatially coupled codes can be employed in this scenario. Furthermore, we derive a scaling law between the gap to capacity, the cardinality of the input and output alphabets, and the required size of the mapper. #R##N#The second one is an integrated scheme in which the code is used both for source coding, in order to create codewords distributed according to the capacity-achieving input distribution, and for channel coding, in order to provide error protection. Such a technique has been recently introduced by Honda and Yamamoto in the context of polar codes, and we show how to apply it also to the design of sparse graph codes. #R##N#The third paradigm is based on an idea of B\"ocherer and Mathar, and separates the two tasks of source coding and channel coding by a chaining construction that binds together several codewords. We present conditions for the source code and the channel code, and we describe how to combine any source code with any channel code that fulfill those conditions, in order to provide capacity-achieving schemes for asymmetric channels. In particular, we show that polar codes, spatially coupled codes, and homophonic codes are suitable as basic building blocks of the proposed coding strategy.

Connected through hyperedge HE-1 (induced by source paper "explicit codes for the wiretap channel a unified design framework"):
  with members:
    - "a new wiretap channel model and its strong secrecy capacity"
    - "empirical and strong coordination via soft covering with polar codes"
    - "coding schemes for achieving strong secrecy at negligible cost"
    - "semantic security capacity for wiretap channels of type ii"
    - "achieving secrecy capacity of the wiretap channel and broadcast channel with a confidential component"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "an information theoretic approach to secret sharing"
    - "polar coding for secret key generation"

Connected through hyperedge HE-2 (induced by source paper "rate constrained shaping codes for structured sources"):
  with members:
    - "divergence optimal fixed to fixed length distribution matching with shell mapping"
    - "performance of optimal data shaping codes"
    - "constant composition distribution matching"
    - "arithmetic distribution matching"
    - "fixed to variable length distribution matching"
    - "informational divergence and entropy rate on rooted trees with probabilities"
    - "matching dyadic distributions to channels"

Connected through hyperedge HE-3 (induced by source paper "sign bit shaping using polar codes"):
  with members:
    - "probabilistically shaped multi level coding with polar codes for fading channels"
    - "code constructions for physical unclonable functions and biometric secrecy systems"
    - "efficient polar code construction for higher order modulation"
    - "polar codes for broadcast channels"
    - "llr based successive cancellation list decoding of polar codes"
    - "achievable rates for shaped bit metric decoding"
    - "list decoding of polar codes"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"

Connected through hyperedge HE-4 (induced by source paper "shaped on off keying using polar codes"):
  with members:
    - "probabilistic parity shaping for linear codes"
    - "protograph based ldpc code design for probabilistic shaping with on off keying"
    - "polar coded pulse position modulation for the poisson channel"
    - "divergence scaling of fixed length binary output one to one distribution matching"
    - "code design for short blocks a survey"
    - "constant composition distribution matching"
    - "list decoding of polar codes"
    - "how to construct polar codes"

Connected through hyperedge HE-5 (induced by source paper "a compressed sensing approach for distribution matching"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"

Connected through hyperedge HE-6 (induced by source paper "a new coding paradigm for the primitive relay channel"):
  with members:
    - "capacity achieving rate compatible polar codes"
    - "asymmetric error correction and flash memory rewriting using polar codes"
    - "capacity of a class of state dependent orthogonal relay channels"
    - "polar coding for the broadcast channel with confidential messages a random binning analogy"
    - "soft decoding based strategies for relay and interference channels analysis and achievable rates using ldpc codes"
    - "universal polar codes"
    - "wireless network information flow a deterministic approach"
    - "nested polar codes for wiretap and relay channels"

Connected through hyperedge HE-7 (induced by source paper "successive wyner ziv coding for the binary ceo problem under logarithmic loss"):
  with members:
    - "successive wyner ziv coding for the binary ceo problem under log loss"
    - "analysis and code design for the binary ceo problem under logarithmic loss"
    - "remote source coding under gaussian noise dueling roles of power and entropy power"
    - "the ceo problem with rth power of difference and logarithmic distortions"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "the non regular ceo problem"
    - "multiterminal source coding under logarithmic loss"
    - "decentralized coding algorithms for distributed storage in wireless sensor networks"

Connected through hyperedge HE-8 (induced by source paper "probabilistically shaped multi level coding with polar codes for fading channels"):
  with members:
    - "polar codes with integrated probabilistic shaping for 5g new radio"
    - "achievable rates for probabilistic shaping"
    - "ultra sparse non binary ldpc codes for probabilistic amplitude shaping"
    - "list decoding of polar codes"

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)