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: "entropy and source coding for integer dimensional singular random variables"
  Abstract: Entropy and differential entropy are important quantities in information theory. A tractable extension to singular random variables—which are neither discrete nor continuous—has not been available so far. Here, we present such an extension for the practically relevant class of integer-dimensional singular random variables. The proposed entropy definition contains the entropy of discrete random variables and the differential entropy of continuous random variables as special cases. We show that it transforms in a natural manner under Lipschitz functions, and that it is invariant under unitary transformations. We define joint entropy and conditional entropy for integer-dimensional singular random variables, and we show that the proposed entropy conveys useful expressions of the mutual information. As first applications of our entropy definition, we present a result on the minimal expected codeword length of quantized integer-dimensional singular sources and a Shannon lower bound for integer-dimensional singular sources.

Connected through hyperedge HE-1 (induced by source paper "minimal achievable sufficient statistic learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "uncertainty in the variational information bottleneck"
    - "estimating information flow in neural networks"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "emergence of invariance and disentanglement in deep representations"
    - "gradient regularization improves accuracy of discriminative models"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "rate distortion theory for general sets and measures"):
  with members:
    - "the shannon lower bound is asymptotically tight"
    - "information theoretic limits of matrix completion"

Connected through hyperedge HE-3 (induced by source paper "lossless linear analog compression"):
  with members:
    - "almost lossless analog signal separation and probabilistic uncertainty relations"
    - "optimal phase transitions in compressed sensing"

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)