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 zero delay source channel coding"
  Abstract: This paper studies the zero-delay source-channel coding problem, and specifically the problem of obtaining the vector transformations that optimally map between the m-dimensional source space and k-dimensional channel space, under a given transmission power constraint and for the mean square error distortion. The functional properties of the cost are studied and the necessary conditions for the optimality of the encoder and decoder mappings are derived. An optimization algorithm that imposes these conditions iteratively, in conjunction with the noisy channel relaxation method to mitigate poor local minima, is proposed. The numerical results show strict improvement over prior methods. The numerical approach is extended to the scenario of source-channel coding with decoder side information. The resulting encoding mappings are shown to be continuous relatives of, and in fact subsume as special case, the Wyner-Ziv mappings encountered in digital distributed source coding systems. A well-known result in information theory pertains to the linearity of optimal encoding and decoding mappings in the scalar Gaussian source and channel setting, at all channel signal-to-noise ratios (CSNRs). In this paper, the linearity of optimal coding, beyond the Gaussian source and channel, is considered and the necessary and sufficient condition for linearity of optimal mappings, given a noise (or source) distribution, and a specified a total power constraint are derived. It is shown that the Gaussian source-channel pair is unique in the sense that it is the only source-channel pair for which the optimal mappings are linear at more than one CSNR values. Moreover, the asymptotic linearity of optimal mappings is shown for low CSNR if the channel is Gaussian regardless of the source and, at the other extreme, for high CSNR if the source is Gaussian, regardless of the channel. The extension to the vector settings is also considered where besides the conditions inherited from the scalar case, additional constraints must be satisfied to ensure linearity of the optimal mappings.

Connected through hyperedge HE-1 (induced by source paper "zero delay source channel coding with a one bit adc front end and correlated side information at the receiver"):
  with members:
    - "joint source channel coding with one bit adc front end"
    - "one bit massive mimo channel estimation and high order modulations"
    - "massive mimo with 1 bit adc"
    - "a deterministic annealing approach to optimization of zero delay source channel codes"
    - "at low snr asymmetric quantizers are better"

Connected through hyperedge HE-2 (induced by source paper "information theoretic approach to strategic communication as a hierarchical game"):
  with members:
    - "on the role of side information in strategic communication"
    - "privacy constrained information processing"
    - "estimation with strategic sensors"

Connected through hyperedge HE-3 (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"
    - "error correcting codes for distributed control"
    - "analog matching of colored sources to colored channels"
    - "joint wyner ziv dirty paper coding by modulo lattice modulation"

Connected through hyperedge HE-4 (induced by source paper "joint source channel coding with one bit adc front end"):
  with members:
    - "one bit massive mimo channel estimation and high order modulations"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "massive mimo with 1 bit adc"
    - "at low snr asymmetric quantizers are better"

Connected through hyperedge HE-5 (induced by source paper "zero delay joint source channel coding in the presence of interference known at the encoder"):
  with members:
    - "optimization of zero delay mappings for distributed coding by deterministic annealing"
    - "on conditions for linearity of optimal estimation"
    - "joint source channel codes for mimo block fading channels"

Connected through hyperedge HE-6 (induced by source paper "information nonanticipative rate distortion function and its applications"):
  with members:
    - "nonanticipative rate distortion function and relations to filtering theory"
    - "applications of information nonanticipative rate distortion function"
    - "directed information on abstract spaces properties and variational equalities"
    - "variational equalities of directed information and applications"
    - "on the relation of nonanticipative rate distortion function and filtering theory"
    - "directed information on abstract spaces properties and extremum problems"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-7 (induced by source paper "deterministic annealing based optimization for zero delay source channel coding in networks"):
  with members:
    - "optimization of zero delay mappings for distributed coding by deterministic annealing"
    - "a deterministic annealing approach to optimization of zero delay source channel codes"
    - "robust analog function computation via wireless multiple access channels"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-8 (induced by source paper "on remote estimation with multiple communication channels"):
  with members:
    - "optimal sensor scheduling and remote estimation over an additive noise channel"
    - "optimal estimation with limited measurements and noisy communication"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

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)