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: "variable length coding with feedback finite length codewords and periodic decoding"
  Abstract: Theoretical analysis has long indicated that feedback improves the error exponent but not the capacity of single-user memoryless channels. Recently Polyanskiy et al. studied the benefit of variable-length feedback with termination (VLFT) codes in the non-asymptotic regime. In that work, achievability is based on an infinite length random code and decoding is attempted at every symbol. The coding rate backoff from capacity due to channel dispersion is greatly reduced with feedback, allowing capacity to be approached with surprisingly small expected latency. This paper is mainly concerned with VLFT codes based on finite-length codes and decoding attempts only at certain specified decoding times. The penalties of using a finite block-length $N$ and a sequence of specified decoding times are studied. This paper shows that properly scaling $N$ with the expected latency can achieve the same performance up to constant terms as with $N = \infty$. The penalty introduced by periodic decoding times is a linear term of the interval between decoding times and hence the performance approaches capacity as the expected latency grows if the interval between decoding times grows sub-linearly with the expected latency.

Connected through hyperedge HE-1 (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-2 (induced by source paper "in band full duplex wireless challenges and opportunities"):
  with members:
    - "cancellation of power amplifier induced nonlinear self interference in full duplex transceivers"
    - "passive self interference suppression for full duplex infrastructure nodes"
    - "advanced self interference cancellation and multiantenna techniques for full duplex radios"
    - "analysis of oscillator phase noise effects on self interference cancellation in full duplex ofdm radio transceivers"
    - "design and characterization of a full duplex multiantenna system for wifi networks"
    - "full duplex transceiver system calculations analysis of adc and linearity challenges"
    - "simultaneous transmission and reception algorithm design and system level performance"
    - "rate gain region and design tradeoffs for full duplex wireless communications"

Connected through hyperedge HE-3 (induced by source paper "reliability based error detection for feedback communication with low latency"):
  with members:
    - "a rate compatible sphere packing analysis of feedback coding with limited retransmissions"

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)