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: "guessing random additive noise decoding with soft detection symbol reliability information sgrand"
  Abstract: In computer communications, discrete data are channel coded to add robustness to noise and then modulated into continuous signals for transmission and reception. In a hard detection setting, received signals are demodulated into discrete data that are provided to a decoder for inference of the transmitted code-word. If additional soft detection information on the quality of the received signal is provided to the decoder, it is known that its use can improve decoding accuracy. Incorporating that information, however, typically comes at the expense of increased algorithmic complexity of the decoder. #R##N#Here we introduce and analyse a mechanism by which soft detection information can be used within Guessing Random Additive Noise Decoding (GRAND) framework in such a way that decoding accuracy is increased, but computational complexity is decreased. The principle envisages an enhanced code-book-independent quantization of soft detection information where demodulated symbols are indicated to be confidently reliable or are labeled as being unreliable. We introduce two algorithms that incorporate this symbol reliability information, one of which identifies a Maximum Likelihood (ML) decoding and the other either reports an ML decoding or an error. We prove that both are capacity-achieving when used with random code-books, and determine both error exponents and asymptotic complexity. These decoding algorithms are suitable for use with any block-code and have complexities that can only reduce as code-book rates increase. With respect to their hard detection counterparts, they are capable of achieving higher rates with lower error probabilities, and have reduced algorithmic complexity.

Connected through hyperedge HE-1 (induced by source paper "ordered reliability bits guessing random additive noise decoding"):
  with members:
    - "guessing random additive noise decoding with symbol reliability information srgrand"
    - "capacity achieving guessing random additive noise decoding"
    - "fast and flexible software polar list decoders"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "5g nr ca polar maximum likelihood decoding by grand"):
  with members:
    - "capacity achieving guessing random additive noise decoding"
    - "llr based successive cancellation list decoding of polar codes"
    - "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)