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: "fixed error asymptotics for erasure and list decoding"
  Abstract: We derive the optimum second-order coding rates, known as second-order capacities, for erasure and list decoding. For erasure decoding for discrete memoryless channels, we show that second-order capacity is $\sqrt{V}\Phi^{-1}(\epsilon_t)$ where $V$ is the channel dispersion and $\epsilon_t$ is the total error probability, i.e., the sum of the erasure and undetected errors. We show numerically that the expected rate at finite blocklength for erasures decoding can exceed the finite blocklength channel coding rate. We also show that the analogous result also holds for lossless source coding with decoder side information, i.e., Slepian-Wolf coding. For list decoding, we consider list codes of deterministic size that scales as $\exp(\sqrt{n}l)$ and show that the second-order capacity is $l+\sqrt{V}\Phi^{-1}(\epsilon)$ where $\epsilon$ is the permissible error probability. We also consider lists of polynomial size $n^\alpha$ and derive bounds on the third-order coding rate in terms of the order of the polynomial $\alpha$. These bounds are tight for symmetric and singular channels. The direct parts of the coding theorems leverage on the simple threshold decoder and converses are proved using variants of the hypothesis testing converse.

Connected through hyperedge HE-1 (induced by source paper "energy efficient coded random access for the wireless uplink"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "massive mimo unsourced random access"
    - "massive random access with common alarm messages"
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive iot connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"

Connected through hyperedge HE-2 (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-3 (induced by source paper "information spectrum approach to strong converse theorems for degraded wiretap channels"):
  with members:
    - "converses for secret key agreement and secure computing"
    - "strong converse and second order asymptotics of channel resolvability"
    - "secret key agreement general capacity and second order asymptotics"
    - "second order coding rates for channels with state"
    - "strong converse for a degraded wiretap channel via active hypothesis testing"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "how many queries will resolve common randomness"
    - "reliability and secrecy functions of the wiretap channel under cost constraint"

Connected through hyperedge HE-4 (induced by source paper "asymmetric evaluations of erasure and undetected error probabilities"):
  with members:
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "list decoding random coding exponents and expurgated exponents"
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "erasure list exponents for slepian wolf decoding"
    - "moderate deviations in channel coding"
    - "fixed length lossy compression in the finite blocklength regime"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"
    - "information spectrum approach to second order coding rate in channel coding"

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)