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 "feedback capacity and coding for the 0 k rll input constrained bec"):
  Abstract: The input-constrained binary erasure channel (BEC) with strictly causal feedback is studied. The channel input sequence must satisfy the $(0,k)$-runlength limited (RLL) constraint, i.e., no more than $k$ consecutive `$0$'s are allowed. The feedback capacity of this channel is derived for all $k\geq 1$, and is given by $$C^\mathrm{fb}_{(0,k)}(\varepsilon) = \max\frac{\overline{\varepsilon}H_2(\delta_0)+\sum_{i=1}^{k-1}\left(\overline{\varepsilon}^{i+1}H_2(\delta_i)\prod_{m=0}^{i-1}\delta_m\right)}{1+\sum_{i=0}^{k-1}\left(\overline{\varepsilon}^{i+1} #R##N#\prod_{m=0}^{i}\delta_m\right)},$$ where $\varepsilon$ is the erasure probability, $\overline{\varepsilon}=1-\varepsilon$ and $H_2(\cdot)$ is the binary entropy function. The maximization is only over $\delta_{k-1}$, while the parameters $\delta_i$ for $i\leq k-2$ are straightforward functions of $\delta_{k-1}$. The lower bound is obtained by constructing a simple coding for all $k\geq1$. It is shown that the feedback capacity can be achieved using zero-error, variable length coding. For the converse, an upper bound on the non-causal setting, where the erasure is available to the encoder just prior to the transmission, is derived. This upper bound coincides with the lower bound and concludes the search for both the feedback capacity and the non-causal capacity. As a result, non-causal knowledge of the erasures at the encoder does not increase the feedback capacity for the $(0,k)$-RLL input-constrained BEC. This property does not hold in general: the $(2,\infty)$-RLL input-constrained BEC, where every `$1$' is followed by at least two `$0$'s, is used to show that the feedback capacity can be strictly greater than the non-causal capacity.

Includes members:
  Member "feedback capacity and coding for the bibo channel with a no repeated ones input constraint":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "a single letter upper bound on the feedback capacity of unifilar finite state channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "an efficient feedback coding scheme with low error probability for discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "active learning and csi acquisition for mmwave initial alignment")
  Member "a simple proof for the optimality of randomized posterior matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active learning and csi acquisition for mmwave initial alignment")
  Member "extrinsic jensen shannon divergence applications to variable length coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low complexity algorithms for transmission of short blocks over the bsc with full feedback")
  Member "modulation coding for flash memories":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "d imbalance wom codes for reduced inter cell interference in multi level nvms")
  Member "capacity and coding for the ising channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "optimal feedback communication via posterior matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "feedback channel communication with low precision arithmetic")

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)