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: "on the input degradedness and input equivalence between channels"
  Abstract: A channel $W$ is said to be input-degraded from another channel $W'$ if $W$ can be simulated from $W'$ by randomization at the input. We provide a necessary and sufficient condition for a channel to be input-degraded from another one. We show that any decoder that is good for $W'$ is also good for $W$. We provide two characterizations for input-degradedness, one of which is similar to the Blackwell-Sherman-Stein theorem. We say that two channels are input-equivalent if they are input-degraded from each other. We study the topologies that can be constructed on the space of input-equivalent channels, and we investigate their properties. Moreover, we study the continuity of several channel parameters and operations under these topologies.

Connected through hyperedge HE-1 (induced by source paper "a novel approach to multivariate redundancy and synergy"):
  with members:
    - "understanding individual neuron importance using information theory"
    - "unique information and secret key agreement"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "secret sharing and shared information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "synergy redundancy and common information"

Connected through hyperedge HE-2 (induced by source paper "unique informations and deficiencies"):
  with members:
    - "computing the unique information"
    - "secret sharing and shared information"
    - "disentangled representations via synergy minimization"
    - "the blackwell relation defines no lattice"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "bivariate measure of redundant information"

Connected through hyperedge HE-3 (induced by source paper "a characterization of the shannon ordering of communication channels"):
  with members:
    - "continuity of channel parameters and operations under various dmc topologies"

Connected through hyperedge HE-4 (induced by source paper "comparison of noisy channels and reverse data processing theorems"):
  with members:
    - "a characterization of the shannon ordering of communication channels"

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)