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: "fast simplified successive cancellation list decoding of polar codes"
  Abstract: Polar codes are capacity achieving error correcting codes that can be decoded through the successive-cancellation algorithm. To improve its error-correction performance, a list-based version called successive-cancellation list (SCL) has been proposed in the past, that however substantially increases the number of time-steps in the decoding process. The simplified SCL (SSCL) decoding algorithm exploits constituent codes within the polar code structure to greatly reduce the required number of time-steps without introducing any error-correction performance loss. In this paper, we propose a faster decoding approach to decode one of these constituent codes, the Rate-1 node. We use this Rate-1 node decoder to develop Fast-SSCL. We demonstrate that only a list-size-bound number of bits needs to be estimated in Rate-1 nodes and Fast-SSCL exactly matches the error-correction performance of SCL and SSCL. This technique can potentially greatly reduce the total number of time-steps needed for polar codes decoding: analysis on a set of case studies show that Fast-SSCL has a number of time-steps requirement that is up to 66.6% lower than SSCL and 88.1% lower than SCL.

Connected through hyperedge HE-1 (induced by source paper "enumeration of minimum hamming weight polar codewords with sublinear complexity"):
  with members:
    - "fast and flexible successive cancellation list decoders for polar codes"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"
    - "an adaptive successive cancellation list decoder for polar codes with cyclic redundancy check"
    - "on finite length performance of polar codes stopping sets error floor and concatenated design"
    - "new techniques for upper bounding the ml decoding performance of binary linear codes"

Connected through hyperedge HE-2 (induced by source paper "efficient bit channel reliability computation for multi mode polar code encoders and decoders"):
  with members:
    - "multi mode unrolled architectures for polar decoders"
    - "symbol decision successive cancellation list decoder for polar codes"
    - "flexible and low complexity encoding and decoding of systematic polar codes"
    - "237 gbit s unrolled hardware polar decoder"
    - "fast polar decoders algorithm and implementation"
    - "list decoding of polar codes"
    - "how to construct polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "fast and flexible successive cancellation list decoders for polar codes"):
  with members:
    - "llr based successive cancellation list decoder for polar codes with multibit decision"
    - "a high throughput list decoder architecture for polar codes"
    - "fast list decoders for polar codes"
    - "symbol decision successive cancellation list decoder for polar codes"
    - "llr based successive cancellation list decoding of polar codes"
    - "fast polar decoders algorithm and implementation"
    - "list decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "blind detection with polar codes"):
  with members:
    - "blind detection of polar codes"
    - "symbol decision successive cancellation list decoder for polar codes"
    - "llr based successive cancellation list decoding of polar codes"
    - "fast polar decoders algorithm and implementation"
    - "list decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

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)