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: "converse theorems for the dmc with mismatched decoding"
  Abstract: The problem of mismatched decoding with an additive metric $q$ for a discrete memoryless channel $W$ is addressed. The "product-space" improvement of the random coding lower bound on the mismatch capacity, $C_q^{(\infty)}(W)$, was introduced by Csiszar and Narayan. #R##N#We study two kinds of decoders. The {\it $\delta$-margin mismatched decoder} outputs a message whose metric with the channel output exceeds that of all the other codewords by at least $\delta$. The {\it $\tau$-threshold decoder} outputs a single message whose metric with the channel output exceeds a threshold $\tau$. Both decoders declare an error if they fail to find a message that meets the requirement. It is assumed that $q$ is bounded. #R##N#It is proved that $C_q^{(\infty)}(W)$ is equal to the mismatch capacity with a constant margin decoder. We next consider sequences of $P$-constant composition codebooks, whose empirical distribution of the codewords are at least $o(n^{-1/2})$ close in the $L_1$ distance sense to $P$. Using the Central Limit Theorem, it is shown that for such sequences of codebooks the supremum of achievable rates with constant threshold decoding is upper bounded by the supremum of the achievable rates with a constant margin decoder, and therefore also by $C_q^{(\infty)}(W)$. #R##N#Further, a soft converse is proved stating that if the average probability of error of a sequence of codebooks converges to zero sufficiently fast, the rate of the code sequence is upper bounded by $C_q^{(\infty)}(W)$. In particular, if $q$ is a bounded rational metric, and the average probability of error converges to zero faster than $O(n^{-1})$, then $R\leq C_q^{(\infty)}(W)$. Finally, a max-min multi-letter upper bound on the mismatch capacity that bears some resemblance to $C_q^{(\infty)}(W)$ is presented.

Connected through hyperedge HE-1 (induced by source paper "ratio list decoding"):
  with members:
    - "list decoding random coding exponents and expurgated exponents"
    - "expurgated random coding ensembles exponents refinements and connections"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a general formula for the mismatch capacity"
    - "list decoding of polar codes"

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)