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: "optimal coding for the deletion channel with small deletion probability"
  Abstract: The deletion channel is the simplest point-to-point communication channel that models lack of synchronization. Input bits are deleted independently with probability d, and when they are not deleted, they are not affected by the channel. Despite significant effort, little is known about the capacity of this channel, and even less about optimal coding schemes. In this paper we develop a new systematic approach to this problem, by demonstrating that capacity can be computed in a series expansion for small deletion probability. We compute three leading terms of this expansion, and find an input distribution that achieves capacity up to this order. This constitutes the first optimal coding result for the deletion channel. #R##N#The key idea employed is the following: We understand perfectly the deletion channel with deletion probability d=0. It has capacity 1 and the optimal input distribution is i.i.d. Bernoulli(1/2). It is natural to expect that the channel with small deletion probabilities has a capacity that varies smoothly with d, and that the optimal input distribution is obtained by smoothly perturbing the i.i.d. Bernoulli(1/2) process. Our results show that this is indeed the case. We think that this general strategy can be useful in a number of capacity calculations.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of channels with timing synchronization errors"):
  with members:
    - "achievable rates for channels with deletions and insertions"
    - "modeling and information rates for synchronization error channels"
    - "write channel model for bit patterned media recording"
    - "coding for high density recording on a 1 d granular magnetic medium"
    - "on the deletion channel with small deletion probability"
    - "novel bounds on the capacity of the binary deletion channel"

Connected through hyperedge HE-2 (induced by source paper "on the capacity of channels with synchronization errors"):
  with members:
    - "achievable rates for channels with deletions and insertions"
    - "write channel model for bit patterned media recording"
    - "coding for high density recording on a 1 d granular magnetic medium"
    - "on the deletion channel with small deletion probability"
    - "novel bounds on the capacity of the binary deletion channel"

Connected through hyperedge HE-3 (induced by source paper "an upper bound on the capacity of non binary deletion channels"):
  with members:
    - "a note on the deletion channel capacity"
    - "novel bounds on the capacity of the binary deletion channel"

Connected through hyperedge HE-4 (induced by source paper "on the capacity of the one bit deletion and duplication channel"):
  with members:
    - "achievable rates for channels with deletions and insertions"
    - "on the deletion channel with small deletion probability"
    - "novel bounds on the capacity of the binary deletion channel"

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)