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: "rate distortion function for a heegard berger problem with two sources and degraded reconstruction sets"
  Abstract: In this work, we investigate an instance of the Heegard-Berger problem with two sources and arbitrarily correlated side information sequences at two decoders, in which the reconstruction sets at the decoders are degraded. Specifically, two sources are to be encoded in a manner that one of the two is reproduced losslessly by both decoders, and the other is reproduced to within some prescribed distortion level at one of the two decoders. We establish a single-letter characterization of the rate-distortion function for this model. The investigation of this result in some special cases also sheds light on the utility of joint compression of the two sources. Furthermore, we also generalize our result to the setting in which the source component that is to be recovered by both users is reconstructed in a lossy fashion, under the requirement that all terminals (i.e., the encoder and both decoders) can share an exact copy of the compressed version of this source component, i.e., a common encoder-decoders reconstruction constraint. For this model as well, we establish a single-letter characterization of the associated rate-distortion function.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "rate distortion region of a gray wyner model with side information"):
  with members:
    - "source coding problems with conditionally less noisy side information"
    - "rate distortion with side information at many decoders"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"
    - "source coding for a simple network with receiver side information"
    - "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 "information theoretic caching sequential coding for computing"):
  with members:
    - "wireless device to device caching networks basic principles and system performance"
    - "a new converse bound for coded caching"
    - "a rate distortion approach to caching"
    - "content caching and delivery over heterogeneous wireless networks"
    - "order optimal rate of caching and coded multicasting with random demands"
    - "fundamental limits of caching with secure delivery"
    - "coded caching for delay sensitive content"
    - "coded caching for multi level popularity and access"

Connected through hyperedge HE-4 (induced by source paper "common reconstructions in the successive refinement problem with receiver side information"):
  with members:
    - "vector gaussian rate distortion with variable side information"
    - "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-5 (induced by source paper "lp bounds for rate distortion with variable side information"):
  with members:
    - "vector gaussian rate distortion with variable side information"
    - "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"

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)