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: "erasure list random coding error exponents are not universally achievable"
  Abstract: We study the problem of universal decoding for unknown discrete memoryless channels in the presence of erasure/list option at the decoder, in the random coding regime. Specifically, we harness a universal version of Forney's classical erasure/list decoder developed in earlier studies, which is based on the competitive minimax methodology, and guarantees universal achievability of a certain fraction of the optimum random coding error exponents. In this paper, we derive an exact single-letter expression for the maximum achievable fraction. Examples are given in which the maximal achievable fraction is strictly less than unity, which imply that, in general, there is no universal erasure/list decoder which achieves the same random coding error exponents as the optimal decoder for a known channel. This is in contrast to the situation in ordinary decoding (without the erasure/list option), where optimum exponents are universally achievable, as is well known. It is also demonstrated that previous lower bounds derived for the maximal achievable fraction are not tight in general. We then analyze a generalized random coding ensemble which incorporate a training sequence, in conjunction with a suboptimal practical decoder ("plug-in" decoder), which first estimates the channel using the known training sequence, and then decodes the remaining symbols of the codeword using the estimated channel. One of the implications of our results, is setting the stage for a reasonable criterion of optimal training. Finally, we compare the performance of the "plug-in" decoder and the universal decoder, in terms of the achievable error exponents, and show that the latter is noticeably better than the former.

Connected through hyperedge HE-1 (induced by source paper "mismatched guesswork"):
  with members:
    - "universal randomized guessing with application to asynchronous decentralized brute force attacks"
    - "why botnets work distributed brute force attacks need no synchronization"
    - "a characterization of guesswork on swiftly tilting curves"
    - "centralized vs decentralized multi agent guesswork"
    - "guesswork subject to a total entropy budget"
    - "multi user guesswork and brute force security"
    - "list decoding random coding exponents and expurgated exponents"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"

Connected through hyperedge HE-2 (induced by source paper "exact error and erasure exponents for the asymmetric broadcast channel"):
  with members:
    - "exact random coding exponents and universal decoders for the asymmetric broadcast channel"
    - "expurgated bounds for the asymmetric broadcast channel"
    - "random coding error exponents for the two user interference channel"
    - "simplified erasure list decoding"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "exact random coding error exponents of optimal bin index decoding"
    - "error exponents for broadcast channels with degraded message sets"
    - "a neyman pearson approach to universal erasure and list decoding"

Connected through hyperedge HE-3 (induced by source paper "random coding error exponents for the two user interference channel"):
  with members:
    - "mismatched multi letter successive decoding for the multiple access channel"
    - "optimal achievable rates for interference networks with random codes"
    - "list decoding random coding exponents and expurgated exponents"
    - "exact correct decoding exponent of the wiretap channel decoder"
    - "exact random coding error exponents of optimal bin index decoding"
    - "multiuser random coding techniques for mismatched decoding"
    - "the generalized random energy model and its application to the statistical physics of ensembles of hierarchical codes"
    - "interference channel capacity region for randomized fixed composition codes"

Connected through hyperedge HE-4 (induced by source paper "channel detection in coded communication"):
  with members:
    - "quickest transient change detection under a sampling constraint"
    - "sampling constrained asynchronous communication how to sleep efficiently"
    - "on zero rate error exponents of finite state channels with input dependent states"
    - "simplified erasure list decoding"
    - "list decoding random coding exponents and expurgated exponents"
    - "asymptotically optimal decision rules for joint detection and source coding"
    - "exact random coding error exponents of optimal bin index decoding"
    - "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding"

Connected through hyperedge HE-5 (induced by source paper "simplified erasure list decoding"):
  with members:
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "list decoding random coding exponents and expurgated exponents"
    - "expurgated random coding ensembles exponents refinements and connections"
    - "erasure list exponents for slepian wolf decoding"
    - "a neyman pearson approach to universal erasure and list decoding"

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)