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: "convolutional code specific crc code design"
  Abstract: Cyclic redundancy check (CRC) codes check if a codeword is correctly received. This paper presents an algorithm to design CRC codes that are optimized for the code-specific error behavior of a specified feedforward convolutional code. The algorithm utilizes two distinct approaches to computing undetected error probability of a CRC code used with a specific convolutional code. The first approach enumerates the error patterns of the convolutional code and tests if each of them is detectable. The second approach reduces complexity significantly by exploiting the equivalence of the undetected error probability to the frame error rate of an equivalent catastrophic convolutional code. The error events of the equivalent convolutional code are exactly the undetectable errors for the original concatenation of CRC and convolutional codes. This simplifies the computation because error patterns do not need to be individually checked for detectability. As an example, we optimize CRC codes for a commonly used 64-state convolutional code for information length k=1024 demonstrating significant reduction in undetected error probability compared to the existing CRC codes with the same degrees. For a fixed target undetected error probability, the optimized CRC codes typically require 2 fewer bits.

Connected through hyperedge HE-1 (induced by source paper "an efficient algorithm for designing optimal crcs for tail biting convolutional codes"):
  with members:
    - "efficient error correcting codes in the short blocklength regime"

Connected through hyperedge HE-2 (induced by source paper "crc selection for decoding of crc polar concatenated codes"):
  with members:
    - "polar code construction for list decoding"
    - "reduced complexity scl decoding of multi crc aided polar codes"
    - "list decoding of polar codes"
    - "an adaptive successive cancellation list decoder for polar codes with cyclic redundancy check"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "efficient error correcting codes in the short blocklength regime"):
  with members:
    - "low complexity joint channel estimation and list decoding of short codes"
    - "low latency short packet transmissions fixed length or harq"
    - "design of polar codes in 5g new radio"
    - "joint design of convolutional code and crc under serial list viterbi decoding"
    - "on the error probability of short concatenated polar and cyclic codes with interleaving"
    - "short packets over block memoryless fading channels pilot assisted or noncoherent transmission"
    - "polar code construction for list decoding"
    - "ultra sparse non binary ldpc codes for probabilistic amplitude shaping"

Connected through hyperedge HE-4 (induced by source paper "variable length convolutional coding for short blocklengths with decision feedback"):
  with members:
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "green communication via type i arq finite block length analysis"
    - "finite block length analysis of the incremental redundancy harq"
    - "reliability output decoding of tail biting convolutional codes"
    - "quasi static simo fading channels at finite blocklength"
    - "a hybrid arq scheme based on polar codes"
    - "feedback communication systems with limitations on incremental redundancy"
    - "reliability based error detection for feedback communication with low latency"

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)