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 transmission and state estimation a constrained channel coding approach"
  Abstract: A scenario involving a source, a channel, and a destination, where the destination is interested in {\em both} reliably reconstructing the message transmitted by the source and estimating with a fidelity criterion the state of the channel, is considered. The source knows the channel statistics, but is oblivious to the actual channel state realization. Herein it is established that a distortion constraint for channel state estimation can be reduced to an additional cost constraint on the source input distribution, in the limit of large coding block length. A newly defined capacity-distortion function thus characterizes the fundamental tradeoff between transmission rate and state estimation distortion. It is also shown that non-coherent communication coupled with channel state estimation conditioned on treating the decoded message as training symbols achieves the capacity-distortion function. Among the various examples considered, the capacity-distortion function for a memoryless Rayleigh fading channel is characterized to within 1.443 bits at high signal-to-noise ratio. The constrained channel coding approach is also extended to multiple access channels, leading to a coupled cost constraint on the input distributions for the transmitting sources.

Connected through hyperedge HE-1 (induced by source paper "joint state sensing and communication over memoryless multiple access channels"):
  with members:
    - "joint state sensing and communication optimal tradeoff for a memoryless case"
    - "dependence balance based outer bounds for gaussian networks with cooperation and feedback"

Connected through hyperedge HE-2 (induced by source paper "on sparse sensing and sparse sampling of coded signals at sub landau rates"):
  with members:
    - "compressed sensing of block sparse positive vectors"
    - "support recovery with sparsely sampled free random matrices"
    - "the jacobi mimo channel"
    - "causal state communication"
    - "subspace methods for joint sparse recovery"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "optimal phase transitions in compressed sensing"
    - "approximate sparsity pattern recovery information theoretic lower bounds"

Connected through hyperedge HE-3 (induced by source paper "gaussian state amplification with noisy state observations"):
  with members:
    - "estimation with a helper who knows the interference"

Connected through hyperedge HE-4 (induced by source paper "action dependent strictly causal state communication"):
  with members:
    - "multiple access channels with states causally known at transmitters"
    - "active classification theory and application to underwater inspection"

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)