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: "vector gaussian rate distortion with variable side information"
  Abstract: We consider rate-distortion with two decoders, each with distinct side information. This problem is well understood when the side information at the decoders satisfies a certain degradedness condition. We consider cases in which this degradedness condition is violated but the source and the side information consist of jointly Gaussian vectors. We provide a hierarchy of four lower bounds on the optimal rate. These bounds are then used to determine the optimal rate for several classes of instances.

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"
    - "broadcasting correlated vector gaussians"
    - "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 "common reconstructions in the successive refinement problem with receiver side information"):
  with members:
    - "rate distortion function for a heegard berger problem with two sources and degraded reconstruction sets"
    - "heegard berger and cascade source coding problems with common reconstruction constraints"
    - "source coding problems with conditionally less noisy side information"
    - "rate distortion with side information at many decoders"
    - "the rate distortion function for product of two sources with side information at decoders"
    - "side information scalable source coding"
    - "on multistage successive refinement for wyner ziv source coding with degraded side informations"

Connected through hyperedge HE-3 (induced by source paper "lp bounds for rate distortion with variable side information"):
  with members:
    - "rate distortion function for a heegard berger problem with two sources and degraded reconstruction sets"
    - "a rate distortion approach to index coding"
    - "source coding problems with conditionally less noisy side information"
    - "the rate distortion function for product of two sources with side information at decoders"
    - "index coding via linear programming"

Connected through hyperedge HE-4 (induced by source paper "a rate distortion approach to index coding"):
  with members:
    - "graph theory versus minimum rank for index coding"
    - "on the capacity region for index coding"
    - "decentralized coded caching attains order optimal memory rate tradeoff"
    - "local graph coloring and index coding"
    - "fundamental limits of caching"
    - "source coding problems with conditionally less noisy side information"
    - "index coding an interference alignment perspective"
    - "dynamic index coding for wireless broadcast networks"

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)