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: "shannon kotel nikov mappings for analog point to point communications"
  Abstract: In this paper an approach to joint source-channel coding (JSCC), named Shannon-Kotel'nikov mappings (S-K mappings), is presented. S-K mappings are (piecewise) continuous, direct source-to-channel mappings operating directly on amplitude continuous, discrete time signals. These mappings include several existing JSCC schemes. There exist many interesting approaches to analog and semi-analog JSCC in the literature that provide both excellent performance as well as robustness to variations in noise level. However, no common theory describing their behavior on a general basis exists at the moment. #R##N#The objective of this paper is to introduce a general theoretical framework for analysis of analog (and semi-analog) mappings. This framework will enable calculation of distortion when applying such schemes on point-to-point links, reveal more about their fundamental nature, and provide conditions indicating how such mappings should be constructed in order to perform well at low and arbitrary complexity and delay. Since this problem is very difficult we are not attempting to provide a complete theory at this stage, but rather establish a starting point from which future research can be developed. The paper considers memoryless sources with an arbitrary continuous unimodal density function and memoryless Gaussian channels. Most results are valid for good channels.

Connected through hyperedge HE-1 (induced by source paper "threshold effects in parameter estimation as phase transitions in statistical mechanics"):
  with members:
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "relations between random coding exponents and the statistical physics of random 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)