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: "maximum likelihood upper bounds on the capacities of discrete information stable channels"
  Abstract: Motivated by a greedy approach for generating {\it{information stable}} processes, we prove a universal maximum likelihood (ML) upper bound on the capacities of discrete information stable channels, including the binary erasure channel (BEC), the binary symmetric channel (BSC) and the binary deletion channel (BDC). The bound is derived leveraging a system of equations obtained via the Karush-Kuhn-Tucker conditions. Intriguingly, for some memoryless channels, e.g., the BEC and BSC, the resulting upper bounds are tight and equal to their capacities. For the BDC, the universal upper bound is related to a function counting the number of possible ways that a length-$\lo$ binary subsequence can be obtained by deleting $n-m$ bits (with $n-m$ close to $nd$ and $d$ denotes the {\it{deletion probability}}) of a length-$n$ binary sequence. To get explicit upper bounds from the universal upper bound, it requires to compute a maximization of the matching functions over a Hamming cube containing all length-$n$ binary sequences. Calculating the maximization exactly is hard. Instead, we provide a combinatorial formula approximating it. Under certain assumptions, several approximations and an {\it{explicit}} upper bound for deletion probability $d\geq 1/2$ are derived.

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"
    - "sharp analytical capacity upper bounds for sticky and related channels"
    - "on the list decodability of insertions and deletions"

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)