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: "analog matching of colored sources to colored channels"
  Abstract: Analog (uncoded) transmission provides a simple and robust scheme for communicating a Gaussian source over a Gaussian channel under the mean squared error (MSE) distortion measure. Unfortunately, its performance is usually inferior to the all-digital, separation-based source-channel coding solution, which requires exact knowledge of the channel at the encoder. The loss comes from the fact that except for very special cases, e.g. white source and channel of matching bandwidth (BW), it is impossible to achieve perfect matching of source to channel and channel to source by linear means. We show that by combining prediction and modulo-lattice operations, it is possible to match any colored Gaussian source to any colored Gaussian noise channel (of possibly different BW), hence achieve Shannon's optimum attainable performance $R(D)=C$. Furthermore, when the source and channel BWs are equal (but otherwise their spectra are arbitrary), this scheme is asymptotically robust in the sense that for high signal-to-noise ratio a single encoder (independent of the noise variance) achieves the optimum performance. The derivation is based upon a recent modulo-lattice modulation scheme for transmitting a Wyner-Ziv source over a dirty-paper channel.

Connected through hyperedge HE-1 (induced by source paper "multi rate control over awgn channels via analog joint source channel coding"):
  with members:
    - "almost practical tree codes"
    - "rate cost tradeoffs in control"
    - "the gaussian channel with noisy feedback near capacity performance via simple interaction"
    - "on zero delay source channel coding"
    - "error correcting codes for distributed control"
    - "joint wyner ziv dirty paper coding by modulo lattice modulation"

Connected through hyperedge HE-2 (induced by source paper "joint unitary triangularization for mimo networks"):
  with members:
    - "physical layer mimo relaying"
    - "hybrid digital analog codes for source channel broadcast of gaussian sources over gaussian channels"
    - "robust joint source channel coding for delay limited applications"
    - "a framework for designing mimo systems with decision feedback equalization or tomlinson harashima precoding"

Connected through hyperedge HE-3 (induced by source paper "optimal linear joint source channel coding with delay constraint"):
  with members:
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"

Connected through hyperedge HE-4 (induced by source paper "a new achievable rate for the gaussian parallel relay channel"):
  with members:
    - "cooperative strategies for the half duplex gaussian parallel relay channel simultaneous relaying versus successive relaying"
    - "approximate capacity of gaussian relay networks"
    - "capacity of a class of deterministic relay channels"

Connected through hyperedge HE-5 (induced by source paper "joint source channel coding with side information using hybrid digital analog codes"):
  with members:
    - "joint wyner ziv dirty paper coding by modulo lattice modulation"
    - "on the distortion snr exponent of some layered transmission schemes"
    - "joint source and channel coding for mimo systems is it better to be robust or quick"
    - "superimposed coded and uncoded transmissions of a gaussian source over the gaussian channel"

Connected through hyperedge HE-6 (induced by source paper "joint wyner ziv dirty paper coding by modulo lattice modulation"):
  with members:
    - "joint source channel coding with side information using hybrid digital analog codes"

Connected through hyperedge HE-7 (induced by source paper "robust joint source channel coding for delay limited applications"):
  with members:
    - "analog codes on graphs"

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)