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: "information embedding meets distributed control"
  Abstract: We consider the problem of information embedding where the encoder modifies a white Gaussian host signal in a power-constrained manner to encode the message, and the decoder recovers both the embedded message and the modified host signal. This extends the recent work of Sumszyk and Steinberg to the continuous-alphabet Gaussian setting. We show that a dirty-paper coding based strategy achieves the optimal rate for perfect recovery of the modified host and the message. We also provide bounds for the extension wherein the modified host signal is recovered only to within a specified distortion. Our results specialized to the zero-rate case provide the tightest known lower bounds on the asymptotic costs for the vector version of a famous open problem in distributed control — the Witsenhausen counterexample. Using this bound, we characterize the asymptotically optimal costs for the vector Witsenhausen problem to within a factor of 1∶3 for all problem parameters, improving on the earlier best known bound of 2.

Connected through hyperedge HE-1 (induced by source paper "capacity bounds on the downlink of symmetric multi relay single receiver c ran networks"):
  with members:
    - "on achievability for downlink cloud radio access networks with base station cooperation"
    - "capacity bounds for diamond networks with an orthogonal broadcast channel"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "secure degrees of freedom of the gaussian diamond wiretap channel"
    - "capacity bounds for a class of diamond networks"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "an information theoretic approach to secret sharing"
    - "is non unique decoding necessary"

Connected through hyperedge HE-2 (induced by source paper "compression with actions"):
  with members:
    - "estimation with a helper who knows the interference"
    - "lossy computing of correlated sources with fractional sampling"
    - "lossy compression of discrete sources via the viterbi algorithm"

Connected through hyperedge HE-3 (induced by source paper "implicit and explicit communication in decentralized control"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "is witsenhausen s counterexample a relevant toy"
    - "the finite dimensional witsenhausen counterexample"
    - "communication requirements for generating correlated random variables"
    - "a deterministic approach to wireless relay networks"
    - "gaussian interference channel capacity to within one bit"

Connected through hyperedge HE-4 (induced by source paper "the finite dimensional witsenhausen counterexample"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "lattice strategies for the dirty multiple access channel"
    - "the price of certainty waterslide curves and the gap to capacity"
    - "a deterministic approach to wireless relay networks"
    - "gaussian interference channel capacity to within one bit"
    - "why block length and delay behave differently if feedback is present"
    - "multiaccess channels with state known to some encoders and independent messages"
    - "cognitive radio an information theoretic perspective"

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)