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 continuous time gaussian channels"
  Abstract: We establish natural connections between continuous-time Gaussian feedback/memory channels and their associated discrete-time versions in the forms of sampling and approximating theorems. It turns out that these connections, together with relevant tools from stochastic calculus, can enhance our understanding of continuous-time Gaussian channels in terms of giving alternative interpretations to some long-held "folklores", recovering known results from new perspectives, and obtaining new results inspired by the insights and ideas that come along with the connections. In particular, we derive the capacity regions of a continuous-time white Gaussian multiple access channel, a continuous-time white Gaussian interference channel, and a continuous-time white Gaussian broadcast channel, furthermore, applying the the sampling and approximating theorems and the ideas and techniques in their proofs, we analyze how feedback affects the capacity regions of families of continuous-time multi-user one-hop Gaussian channels: feedback will increase the capacity regions of some continuous-time white Gaussian broadcast and interference channels, while it will not increase capacity regions of continuous-time white Gaussian multiple access channels.

Connected through hyperedge HE-1 (induced by source paper "on sampling continuous time gaussian channels"):
  with members:
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "an elementary proof of a classical information theoretic formula"):
  with members:
    - "feedback capacity of stationary gaussian channels"
    - "mutual information and minimum mean square error in gaussian 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)