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 "capacity of burst noise erasure channels with and without feedback and input cost"):
  Abstract: A class of burst noise-erasure channels which incorporate both errors and erasures during transmission is studied. The channel, whose output is explicitly expressed in terms of its input and a stationary ergodic noise-erasure process, is shown to have a so-called "quasi-symmetry" property under certain invertibility conditions. As a result, it is proved that a uniformly distributed input process maximizes the channel's block mutual information, resulting in a closed-form formula for its non-feedback capacity in terms of the noise-erasure entropy rate and the entropy rate of an auxiliary erasure process. The feedback channel capacity is also characterized, showing that feedback does not increase capacity and generalizing prior related results. The capacity-cost function of the channel with and without feedback is also investigated. A sequence of finite-letter upper bounds for the capacity-cost function without feedback is derived. Finite-letter lower bonds for the capacity-cost function with feedback are obtained using a specific encoding rule. Based on these bounds, it is demonstrated both numerically and analytically that feedback can increase the capacity-cost function for a class of channels with Markov noise-erasure processes.

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 "asymptotics of input constrained erasure channel capacity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computable lower bounds for capacities of input driven finite state channels")
  Member "feedback does not increase the capacity of compound channels with additive noise":
    also connected through other hyperedges:
      (none)
  Member "can feedback increase the capacity of the energy harvesting channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "feedback capacity and coding for the bibo channel with a no repeated ones input constraint")
  Member "zero delay sequential transmission of markov sources over burst erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal multiplexed erasure codes for streaming messages with different decoding delays")
  Member "windowed decoding of protograph based ldpc convolutional codes over erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finite length scaling of spatially coupled ldpc codes under window decoding over the bec")
  Member "capacity and coding for the ising channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "a coding theorem for a class of stationary channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance of small probability events in big data information measures applications and challenges")

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)