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: "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities"
  Abstract: In this paper, we consider single- and multi-user Gaussian channels with feedback under expected power constraints and with non-vanishing error probabilities. In the first of two contributions, we study asymptotic expansions for the additive white Gaussian noise (AWGN) channel with feedback under the average error probability formalism. By drawing ideas from Gallager and Nakibo\u{g}lu's work for the direct part and the meta-converse for the converse part, we establish the $\varepsilon$-capacity and show that it depends on $\varepsilon$ in general and so the strong converse fails to hold. Furthermore, we provide bounds on the second-order term in the asymptotic expansion. We show that for any positive integer $L$, the second-order term is bounded between a term proportional to $-\ln_{(L)} n$ (where $\ln_{(L)}(\cdot)$ is the $L$-fold nested logarithm function) and a term proportional to $+\sqrt{n\ln n}$ where $n$ is the blocklength. The lower bound on the second-order term shows that feedback does provide an improvement in the maximal achievable rate over the case where no feedback is available. In our second contribution, we establish the $\varepsilon$-capacity region for the AWGN multiple access channel (MAC) with feedback under the expected power constraint by combining ideas from hypothesis testing, information spectrum analysis, Ozarow's coding scheme, and power control.

Connected through hyperedge HE-1 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback"):
  with members:
    - "common message broadcast channels with feedback in the nonasymptotic regime full feedback"
    - "on awgn channels and gaussian macs with variable length feedback"
    - "on gaussian macs with variable length feedback and non vanishing error probabilities"
    - "variable length coding with stop feedback for the common message broadcast channel in the nonasymptotic regime"
    - "on using feedback in a gaussian channel"
    - "broadcasting a common message with variable length stop feedback codes"
    - "opportunistic capacity and error exponent regions for compound channel with feedback"
    - "error exponents for the gaussian channel with active noisy feedback"

Connected through hyperedge HE-2 (induced by source paper "a tight upper bound on the second order coding rate of parallel gaussian channels with feedback"):
  with members:
    - "the third order term in the normal approximation for the awgn channel"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "information spectrum approach to second order coding rate in channel coding"
    - "simple channel coding bounds"

Connected through hyperedge HE-3 (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"
    - "nonasymptotic coding rate bounds for binary erasure channels with 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 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-4 (induced by source paper "on awgn channels and gaussian macs with variable length feedback"):
  with members:
    - "nonasymptotic coding rate bounds for binary erasure channels with feedback"
    - "variable length coding with stop feedback for the common message broadcast channel in the nonasymptotic regime"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "strong converse theorems for multimessage networks with tight cut set bound"):
  with members:
    - "new strong converse for asymmetric broadcast channels"
    - "achievable rates for gaussian degraded relay channels with non vanishing error probabilities"
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "on the reliability function of the discrete memoryless relay channel"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"
    - "wireless network information flow a deterministic approach"
    - "reliability in source coding with side information"
    - "capacity of a class of modulo sum relay channels"

Connected through hyperedge HE-6 (induced by source paper "achievable rates for gaussian degraded relay channels with non vanishing error probabilities"):
  with members:
    - "on the reliability function of the discrete memoryless relay channel"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "optimum power control at finite blocklength"
    - "simple channel coding bounds"

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)