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: "partitioned successive cancellation flip decoding of polar codes"
  Abstract: Polar codes are a class of channel capacity achieving codes that has been selected for the next generation of wireless communication standards. Successive-cancellation (SC) is the first proposed decoding algorithm, suffering from mediocre error-correction performance at moderate code lengths. In order to improve the error-correction performance of SC, two approaches are available: (i) SC-List decoding which keeps a list of candidates by running a number of SC decoders in parallel, thus increasing the implementation complexity, and (ii) SC-Flip decoding that relies on a single SC module, and keeps the computational complexity close to SC. In this work, we propose the partitioned SC-Flip (PSCF) decoding algorithm, which outperforms SC-Flip in terms of error-correction performance and average computational complexity, leading to higher throughput and reduced energy consumption per codeword. We also introduce a partitioning scheme that best suits our PSCF decoder. Simulation results show that at equivalent frame error rate, PSCF has up to 4.1x less computational complexity than the SC-Flip decoder. At equivalent average number of iterations, the error-correction performance of PSCF outperforms SC-Flip by up to 0.26 dB at frame error rate of 10^-3.

Connected through hyperedge HE-1 (induced by source paper "deep learning aided successive cancellation decoding of polar codes"):
  with members:
    - "neural dynamic successive cancellation flip decoding of polar codes"
    - "improved bit flipping algorithm for successive cancellation decoding of polar codes"
    - "neural belief propagation decoding of crc polar concatenated codes"
    - "deep learning methods for improved decoding of linear codes"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "dynamic scflip decoding of polar codes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "llr based successive cancellation list decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "neural dynamic successive cancellation flip decoding of polar codes"):
  with members:
    - "improved bit flipping algorithm for successive cancellation decoding of polar codes"
    - "neural belief propagation decoding of crc polar concatenated codes"
    - "deep learning methods for improved decoding of linear codes"
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "dynamic scflip decoding of polar codes"
    - "neural offset min sum decoding"
    - "tensorflow a system for large scale machine learning"
    - "llr based successive cancellation list decoding of polar codes"

Connected through hyperedge HE-3 (induced by source paper "improved bit flipping algorithm for successive cancellation decoding of polar codes"):
  with members:
    - "improved successive cancellation flip decoding of polar codes based on error distribution"
    - "dynamic scflip decoding of polar codes"
    - "an improved scflip decoder for polar codes"
    - "partitioned list decoding of polar codes analysis and improvement of finite length performance"
    - "polarbear a 28 nm fd soi asic for decoding of polar codes"
    - "on error correction performance and implementation of polar code list decoders for 5g"
    - "fast and flexible successive cancellation list decoders for polar codes"
    - "progressive bit flipping decoding of polar codes over layered critical sets"

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)