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: "an outer bound for the vector gaussian ceo problem"
  Abstract: We study the vector Gaussian CEO problem, where there are an arbitrary number of agents each having a noisy observation of a vector Gaussian source. The goal of the agents is to describe the source to a central unit, which wants to reconstruct the source within a given distortion. The rate-distortion region of the vector Gaussian CEO problem is unknown in general. Here, we provide an outer bound for the rate-distortion region of the vector Gaussian CEO problem. We obtain our outer bound by evaluating an outer bound for the multi-terminal source coding problem by means of a technique relying on the de Bruijn identity and the properties of the Fisher information. Next, we show that our outer bound strictly improves upon the existing outer bounds for all system parameters. We show this strict improvement by providing a specific example, and showing that there exists a gap between our outer bound and the existing outer bounds. Although our outer bound improves upon the existing outer bounds, we show that our outer bound does not provide the exact rate-distortion region in general. To this end, we provide an example and show that the rate-distortion region is strictly contained in our outer bound for this example.

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 "vector gaussian ceo problem under logarithmic loss"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss and applications"
    - "universality of logarithmic loss in lossy compression"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "justification of logarithmic loss via the benefit of side information"
    - "rate region of the vector gaussian ceo problem with the trace distortion constraint"
    - "secure source coding with a public helper"
    - "multiterminal source coding under logarithmic loss"
    - "multiterminal source coding with an entropy based distortion measure"

Connected through hyperedge HE-3 (induced by source paper "optimal rate exponent region for a class of hypothesis testing against conditional independence problems"):
  with members:
    - "distributed hypothesis testing with concurrent detections"
    - "on hypothesis testing against independence with multiple decision centers"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "multiterminal source coding under logarithmic loss"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-4 (induced by source paper "vector gaussian ceo problem under logarithmic loss and applications"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss"
    - "chaining mutual information and tightening generalization bounds"
    - "on the universality of the logistic loss function"
    - "minimax learning for remote prediction"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "universality of logarithmic loss in lossy compression"
    - "a generalization of blahut arimoto algorithm to compute rate distortion regions of multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-5 (induced by source paper "the ceo problem with rth power of difference and logarithmic distortions"):
  with members:
    - "remote source coding under gaussian noise dueling roles of power and entropy power"
    - "universality of logarithmic loss in lossy compression"
    - "the shannon lower bound is asymptotically tight"
    - "the non regular ceo problem"
    - "network compression worst case analysis"
    - "multiterminal source coding under logarithmic loss"
    - "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "on the capacity of cloud radio access networks with oblivious relaying"):
  with members:
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "opening the black box of deep neural networks via information"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "an uplink downlink duality for cloud radio access network"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "deep learning and the information bottleneck principle"
    - "on discrete alphabets for the two user gaussian interference channel with one receiver lacking knowledge of the interfering codebook"

Connected through hyperedge HE-7 (induced by source paper "distributed information bottleneck method for discrete and gaussian sources"):
  with members:
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "privacy constrained remote source coding"
    - "deep learning and the information bottleneck principle"
    - "multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-8 (induced by source paper "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"):
  with members:
    - "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "short message noisy network coding with a decode forward option"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "wireless network information flow a deterministic approach"
    - "multiterminal source coding under logarithmic loss"

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)