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 "the porosity of additive noise sequences"):
  Abstract: Consider a binary additive noise channel with noiseless feedback. When the noise is a stationary and ergodic process $\mathbf{Z}$, the capacity is $1-\mathbb{H}(\mathbf{Z})$ ($\mathbb{H}(\cdot)$ denoting the entropy rate). It is shown analogously that when the noise is a deterministic sequence $z^\infty$, the capacity under finite-state encoding and decoding is $1-\bar{\rho}(z^\infty)$, where $\bar{\rho}(\cdot)$ is Lempel and Ziv's finite-state compressibility. This quantity is termed the \emph{porosity} $\underline{\sigma}(\cdot)$ of an individual noise sequence. A sequence of schemes are presented that universally achieve porosity for any noise sequence. These converse and achievability results may be interpreted both as a channel-coding counterpart to Ziv and Lempel's work in universal source coding, as well as an extension to the work by Lomnitz and Feder and Shayevitz and Feder on communication across modulo-additive channels. Additionally, a slightly more practical architecture is suggested that draws a connection with finite-state predictability, as introduced by Feder, Gutman, and Merhav.

Includes members:
  Member "zero rate feedback can achieve the empirical capacity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning an adversary s actions for secret communication")
  Member "achieving the empirical capacity using feedback part i memoryless additive models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal communication over arbitrarily varying channels")

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)