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: "performance comparison of short length error correcting codes"
  Abstract: We compare the performance of short-length linear binary codes on the binary erasure channel and the binary-input Gaussian channel. We use a universal decoder that can decode any linear binary block code: Gaussian-elimination based Maximum-Likelihood decoder on the erasure channel and probabilistic Ordered Statistics Decoder on the Gaussian channel. As such we compare codes and not decoders. The word error rate versus the channel parameter is found for LDPC, Reed-Muller, Polar, and BCH codes at length 256 bits. BCH codes outperform other codes in absence of cyclic redundancy check. Under joint decoding, the concatenation of a cyclic redundancy check makes all codes perform very close to optimal lower bounds.

Connected through hyperedge HE-1 (induced by source paper "segmentation discarding ordered statistic decoding for linear block codes"):
  with members:
    - "short block length codes for ultra reliable low latency communications"
    - "code design for short blocks a survey"

Connected through hyperedge HE-2 (induced by source paper "short packet downlink transmission with non orthogonal multiple access"):
  with members:
    - "an optimization perspective of the superiority of noma compared to conventional oma"
    - "optimal joint power and subcarrier allocation for full duplex multicarrier non orthogonal multiple access systems"
    - "wirelessly powered communication networks with short packets"
    - "energy efficient packet scheduling with finite blocklength codes convexity analysis and efficient algorithms"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "enhancing the physical layer security of non orthogonal multiple access in large scale networks"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "non orthogonal multiple access for degraded broadcast channels ra cema"

Connected through hyperedge HE-3 (induced by source paper "decoding reed muller codes using minimum weight parity checks"):
  with members:
    - "code design for short blocks a survey"
    - "list decoding of polar codes"
    - "decomposition methods for large scale lp decoding"
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "adaptive methods for linear programming decoding"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

Connected through hyperedge HE-4 (induced by source paper "performance bounds of concatenated polar coding schemes"):
  with members:
    - "parity check polar coding for 5g and beyond"
    - "deep learning methods for improved decoding of linear codes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "on deep learning based channel decoding"
    - "coding in the finite blocklength regime bounds based on laplace integrals and their asymptotic approximations"
    - "learning to decode linear codes using deep learning"
    - "code design for short blocks a survey"
    - "symbol decision successive cancellation list decoder for 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)