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: "broadcasting correlated vector gaussians"
  Abstract: The problem of sending two correlated vector Gaussian sources over a bandwidth-matched two-user scalar Gaussian broadcast channel is studied in this work, where each receiver wishes to reconstruct its target source under a covariance distortion constraint. We derive a lower bound on the optimal tradeoff between the transmit power and the achievable reconstruction distortion pair. Our derivation is based on a new bounding technique which involves the introduction of appropriate remote sources. Furthermore, it is shown that this lower bound is achievable by a class of hybrid schemes for the special case where the weak receiver wishes to reconstruct a scalar source under the mean squared error distortion constraint.

Connected through hyperedge HE-1 (induced by source paper "vector gaussian successive refinement with degraded side information"):
  with members:
    - "the sum rate of vector gaussian multiple description coding with tree structured covariance distortion constraints"
    - "vector gaussian rate distortion with variable side information"
    - "new results on multiple input multiple output broadcast channels with confidential messages"
    - "private broadcasting over independent parallel channels"
    - "information theoretic proofs of entropy power inequalities"
    - "secure lossy transmission of vector gaussian sources"
    - "capacity equivocation region of the gaussian mimo wiretap channel"
    - "capacity region of gaussian mimo broadcast channels with common and confidential messages"

Connected through hyperedge HE-2 (induced by source paper "communicating correlated sources over mac and interference channels i separation based schemes"):
  with members:
    - "communicating correlated sources over an interference channel"
    - "matched multiuser gaussian source channel communications via uncoded schemes"
    - "on joint source channel coding for correlated sources over multiple access relay channels"
    - "an achievable rate distortion region for the multiple descriptions problem"
    - "a new data processing inequality and its applications in distributed source and channel coding"
    - "optimality and approximate optimality of source channel separation in networks"
    - "interference channels with arbitrarily correlated sources"
    - "distributed joint source channel coding on a multiple access channel with side information"

Connected through hyperedge HE-3 (induced by source paper "a source channel separation theorem with application to the source broadcast problem"):
  with members:
    - "outer bounds on the admissible source region for broadcast channels with correlated sources"
    - "hybrid digital analog codes for source channel broadcast of gaussian sources over gaussian channels"
    - "optimality and approximate optimality of source channel separation in networks"
    - "approximate characterizations for the gaussian source broadcast distortion region"
    - "broadcasting correlated gaussians"
    - "an extremal inequality motivated by multiterminal information theoretic problems"
    - "vector gaussian multiple description with individual and central receivers"

Connected through hyperedge HE-4 (induced by source paper "distributed multilevel diversity coding"):
  with members:
    - "multilevel diversity coding with regeneration"
    - "symmetrical multilevel diversity coding and subset entropy inequalities"
    - "secure symmetrical multilevel diversity coding"
    - "asymmetric multilevel diversity coding and asymmetric gaussian multiple descriptions"
    - "approximating the gaussian multiple description rate region under symmetric distortion constraints"
    - "robust distributed source coding"

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)