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: "reliability based error detection for feedback communication with low latency"
  Abstract: This paper presents a reliability-based decoding scheme for variable-length coding with feedback and demonstrates via simulation that it can achieve higher rates than Polyanskiy et al.'s random coding lower bound for variable-length feedback (VLF) coding on both the BSC and AWGN channel. The proposed scheme uses the reliability output Viterbi algorithm (ROVA) to compute the word error probability after each decoding attempt, which is compared against a target error threshold and used as a stopping criterion to terminate transmission. The only feedback required is a single bit for each decoding attempt, informing the transmitter whether the ROVA-computed word-error probability is sufficiently low. Furthermore, the ROVA determines whether transmission/decoding may be terminated without the need for a rate-reducing CRC.

Connected through hyperedge HE-1 (induced by source paper "asymptotic estimates in information theory with non vanishing error probabilities"):
  with members:
    - "second order asymptotics for the gaussian mac with degraded message sets"
    - "the third order term in the normal approximation for the awgn channel"
    - "strong converse and second order asymptotics of channel resolvability"
    - "channels with cost constraints strong converse and dispersion"
    - "secret key agreement general capacity and second order asymptotics"
    - "second order coding rates for channels with state"
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"

Connected through hyperedge HE-2 (induced by source paper "variable length convolutional coding for short blocklengths with decision feedback"):
  with members:
    - "convolutional code specific crc code design"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "fixed error asymptotics for erasure and list decoding"):
  with members:
    - "the third order term in the normal approximation for the awgn channel"
    - "list decoding random coding exponents and expurgated exponents"
    - "on the dispersions of three network information theory problems"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "feedback communication systems with limitations on incremental redundancy"
    - "erasure list exponents for slepian wolf decoding"
    - "the third order term in the normal approximation for singular channels"
    - "the log volume of optimal codes for memoryless channels asymptotically within a few nats"

Connected through hyperedge HE-4 (induced by source paper "reliability output decoding of tail biting convolutional codes"):
  with members:
    - "efficient convergent maximum likelihood decoding on tail biting trellises"

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)