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: "joint source channel codes for mimo block fading channels"
  Abstract: We consider transmission of a continuous amplitude source over an L-block Rayleigh fading $M_t \times M_r$ MIMO channel when the channel state information is only available at the receiver. Since the channel is not ergodic, Shannon's source-channel separation theorem becomes obsolete and the optimal performance requires a joint source -channel approach. Our goal is to minimize the expected end-to-end distortion, particularly in the high SNR regime. The figure of merit is the distortion exponent, defined as the exponential decay rate of the expected distortion with increasing SNR. We provide an upper bound and lower bounds for the distortion exponent with respect to the bandwidth ratio among the channel and source bandwidths. For the lower bounds, we analyze three different strategies based on layered source coding concatenated with progressive, superposition or hybrid digital/analog transmission. In each case, by adjusting the system parameters we optimize the distortion exponent as a function of the bandwidth ratio. We prove that the distortion exponent upper bound can be achieved when the channel has only one degree of freedom, that is L=1, and $\min\{M_t,M_r\}=1$. When we have more degrees of freedom, our achievable distortion exponents meet the upper bound for only certain ranges of the bandwidth ratio. We demonstrate that our results, which were derived for a complex Gaussian source, can be extended to more general source distributions as well.

Connected through hyperedge HE-1 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "deep learning based polar code design"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for joint source channel coding of text"

Connected through hyperedge HE-2 (induced by source paper "deep joint source channel coding for wireless image transmission"):
  with members:
    - "an autoencoder based learned image compressor description of challenge proposal by nctu"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional autoencoder based lossy image compression"
    - "deep learning methods for improved decoding of linear codes"
    - "ai benchmark running deep neural networks on android smartphones"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-3 (induced by source paper "zero delay joint source channel coding in the presence of interference known at the encoder"):
  with members:
    - "on zero delay source channel coding"
    - "optimization of zero delay mappings for distributed coding by deterministic annealing"
    - "on conditions for linearity of optimal estimation"

Connected through hyperedge HE-4 (induced by source paper "throughput and delay analysis in video streaming over block fading channels"):
  with members:
    - "erasure coding for real time streaming"
    - "joint coding and scheduling optimization in wireless systems with varying delay sensitivities"
    - "diversity embedded streaming erasure codes de sco constructions and optimality"
    - "distortion minimization in gaussian layered broadcast coding with successive refinement"

Connected through hyperedge HE-5 (induced by source paper "delay distortion power trade offs in quasi stationary source transmission over block fading channels"):
  with members:
    - "delay limited source and channel coding of quasi stationary sources over block fading channels design and scaling laws"
    - "on the distortion snr exponent of some layered transmission schemes"

Connected through hyperedge HE-6 (induced by source paper "distortion exponent in mimo fading channels with time varying source side information"):
  with members:
    - "joint source channel coding with time varying channel and side information"
    - "distortion outage probability in mimo block fading channels"
    - "distortion exponent in mimo channels with feedback"
    - "distortion minimization in gaussian layered broadcast coding with successive refinement"
    - "joint source channel coding with side information using hybrid digital analog codes"
    - "on the distortion snr exponent of some layered transmission schemes"

Connected through hyperedge HE-7 (induced by source paper "the streaming dmt of fading channels"):
  with members:
    - "erasure coding for real time streaming"
    - "streaming codes for channels with burst and isolated erasures"
    - "linear error correcting codes with anytime reliability"
    - "diversity embedded streaming erasure codes de sco constructions and optimality"
    - "on successive refinement of diversity for fading isi channels"
    - "distortion minimization in gaussian layered broadcast coding with successive refinement"
    - "high snr analysis of outage limited communications with bursty and delay limited information"
    - "on the distortion snr exponent of some layered transmission schemes"

Connected through hyperedge HE-8 (induced by source paper "joint source channel coding with time varying channel and side information"):
  with members:
    - "minimum expected distortion in gaussian source coding with fading side information"
    - "joint source channel coding with correlated interference"
    - "wyner ziv coding over broadcast channels digital schemes"
    - "distortion minimization in gaussian layered broadcast coding with successive refinement"
    - "joint source channel coding with side information using hybrid digital analog codes"
    - "on the distortion snr exponent of some layered transmission schemes"

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)