human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on convexity of error rates in digital communications"):
  Abstract: Convexity properties of error rates of a class of decoders, including the maximum-likelihood/min-distance one as a special case, are studied for arbitrary constellations, bit mapping, and coding. Earlier results obtained for the additive white Gaussian noise channel are extended to a wide class of noise densities, including unimodal and spherically invariant noise. Under these broad conditions, symbol and bit error rates are shown to be convex functions of the signal-to-noise ratio (SNR) in the high-SNR regime with an explicitly determined threshold, which depends only on the constellation dimensionality and minimum distance, thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way. It is the decreasing nature of the noise power density around the decision region boundaries that ensures the convexity of symbol error rates in the general case. The known high/low-SNR bounds of the convexity/concavity regions are tightened and no further improvement is shown to be possible in general. The high-SNR bound fits closely into the channel coding theorem: all codes, including capacity-achieving ones, whose decision regions include the hardened noise spheres (from the noise sphere hardening argument in the channel coding theorem), satisfy this high-SNR requirement and thus has convex error rates in both SNR and noise power. We conjecture that all capacity-achieving codes have convex error rates. Convexity properties in signal amplitude and noise power are also investigated. Some applications of the results are discussed. In particular, it is shown that fading is convexity-preserving and is never good in low dimensions under spherically invariant noise, which may also include any linear diversity combining.

Includes members:
  Member "error rates of capacity achieving codes are convex":
    also connected through other hyperedges:
      (none)
  Member "error rates of the maximum likelihood detector for arbitrary constellations convex concave behavior and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new representation for the symbol error rate")
  Member "bit error rate is convex at high snr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mimo interference alignment over correlated channels with imperfect csi")
  Member "on node density outage probability tradeoff in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized interference alignment part ii application to wireless secrecy")
  Member "log concavity property of the error probability with application to local bounds for wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure diversity multiplexing tradeoff of zero forcing transmit scheme at finite snr")
  Member "on optimum power allocation for the v blast":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimum power and rate allocation for coded v blast average optimization")
  Member "symbol error rates of maximum likelihood detector convex concave behavior and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "error rates of capacity achieving codes are convex")

Question: Please tell me which class the source paper 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)