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: "achievable rates for noisy channels with synchronization errors"
  Abstract: We develop several lower bounds on the capacity of binary input symmetric output channels with synchronization errors which also suffer from other types of impairments such as substitutions, erasures, additive white Gaussian noise (AWGN) etc. More precisely, we show that if the channel with synchronization errors can be decomposed into a cascade of two channels where only the first one suffers from synchronization errors and the second one is a memoryless channel, a lower bound on the capacity of the original channel in terms of the capacity of the synchronization error-only channel can be derived. To accomplish this, we derive lower bounds on the mutual information rate between the transmitted and received sequences (for the original channel) for an arbitrary input distribution, and then relate this result to the channel capacity. The results apply without the knowledge of the exact capacity achieving input distributions. A primary application of our results is that we can employ any lower bound derived on the capacity of the first channel (synchronization error channel in the decomposition) to find lower bounds on the capacity of the (original) noisy channel with synchronization errors. We apply the general ideas to several specific classes of channels such as synchronization error channels with erasures and substitutions, with symmetric q-ary outputs and with AWGN explicitly, and obtain easy-to-compute bounds. We illustrate that, with our approach, it is possible to derive tighter capacity lower bounds compared to the currently available bounds in the literature for certain classes of channels, e.g., deletion/substitution channels and deletion/AWGN channels (for certain signal to noise ratio (SNR) ranges).

Connected through hyperedge HE-1 (induced by source paper "an overview of capacity results for synchronization channels"):
  with members:
    - "coding for deletion channels with multiple traces"
    - "coded trace reconstruction"
    - "coded trace reconstruction in a constant number of traces"
    - "optimally resilient codes for list decoding from insertions and deletions"
    - "zero error capacity of duplication channels"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "maximum likelihood upper bounds on the capacities of discrete information stable channels"
    - "sharp analytical capacity upper bounds for sticky and related channels"

Connected through hyperedge HE-2 (induced by source paper "sharp analytical capacity upper bounds for sticky and related channels"):
  with members:
    - "optimal document exchange and new codes for small number of insertions and deletions"
    - "capacity upper bounds for deletion type channels"
    - "on the capacity of channels with timing synchronization errors"
    - "bounds on the capacity of random insertion and deletion additive noise channels"
    - "achievable rates for channels with deletions and insertions"
    - "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)