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: "nonasymptotic coding rate bounds for binary erasure channels with feedback"
  Abstract: We present nonasymptotic achievability and converse bounds on the maximum coding rate (for a fixed average error probability and a fixed average blocklength) of variable-length full-feedback (VLF) and variable-length stop-feedback (VLSF) codes operating over a binary erasure channel (BEC). For the VLF setup, the achievability bound relies on a scheme that maps each message onto a variable-length Huffman codeword and then repeats each bit of the codeword until it is received correctly. The converse bound is inspired by the meta-converse framework by Polyanskiy, Poor, and Verd\'u (2010) and relies on binary sequential hypothesis testing. For the case of zero error probability, our achievability and converse bounds match. For the VLSF case, we provide achievability bounds that exploit the following feature of BEC: the decoder can assess the correctness of its estimate by verifying whether the chosen codeword is the only one that is compatible with the erasure pattern. One of these bounds is obtained by analyzing the performance of a variable-length extension of random linear fountain codes. The gap between the VLSF achievability and the VLF converse bound, when number of messages is small, is significant: $23\%$ for 8 messages on a BEC with erasure probability $0.5.$ The absence of a tight VLSF converse bound does not allow us to assess whether this gap is fundamental.

Connected through hyperedge HE-1 (induced by source paper "common message broadcast channels with feedback in the nonasymptotic regime full feedback"):
  with members:
    - "on the reliability function of the common message broadcast channel with variable length feedback"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "broadcasting a common message with variable length stop feedback codes"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "a simple converse of burnashev s reliability function"
    - "feedback capacity of the compound channel"

Connected through hyperedge HE-2 (induced by source paper "on gaussian macs with variable length feedback and non vanishing error probabilities"):
  with members:
    - "common message broadcast channels with feedback in the nonasymptotic regime full feedback"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "variable length coding with stop feedback for the common message broadcast channel in the nonasymptotic regime"
    - "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities"
    - "on using feedback in a gaussian channel"
    - "broadcasting a common message with variable length stop feedback codes"
    - "on the dispersions of three network information theory problems"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-3 (induced by source paper "on awgn channels and gaussian macs with variable length feedback"):
  with members:
    - "variable length coding with stop feedback for the common message broadcast channel in the nonasymptotic regime"
    - "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities"
    - "on using feedback in a gaussian channel"
    - "broadcasting a common message with variable length stop feedback codes"
    - "on the dispersions of three network information theory problems"
    - "capacity and coding for the ising channel with feedback"
    - "optimal feedback communication via posterior matching"
    - "error exponents for variable length block codes with feedback and cost constraints"

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)