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: "generalized independent component analysis over finite alphabets"
  Abstract: Independent component analysis (ICA) is a statistical method for transforming an observable multidimensional random vector into components that are as statistically independent as possible from each other.Usually the ICA framework assumes a model according to which the observations are generated (such as a linear transformation with additive noise). ICA over finite fields is a special case of ICA in which both the observations and the independent components are over a finite alphabet. In this work we consider a generalization of this framework in which an observation vector is decomposed to its independent components (as much as possible) with no prior assumption on the way it was generated. This generalization is also known as Barlow's minimal redundancy representation problem and is considered an open problem. We propose several theorems and show that this NP hard problem can be accurately solved with a branch and bound search tree algorithm, or tightly approximated with a series of linear problems. Our contribution provides the first efficient and constructive set of solutions to Barlow's problem.The minimal redundancy representation (also known as factorial code) has many applications, mainly in the fields of Neural Networks and Deep Learning. The Binary ICA (BICA) is also shown to have applications in several domains including medical diagnosis, multi-cluster assignment, network tomography and internet resource management. In this work we show this formulation further applies to multiple disciplines in source coding such as predictive coding, distributed source coding and coding of large alphabet sources.

Connected through hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "streaming ptas for binary ell_0 low rank approximation"):
  with members:
    - "streaming ptas for constrained k means"
    - "approximation schemes for low rank binary matrix approximation problems"
    - "a unified framework for clustering constrained data without locality property"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "faster algorithms for the constrained k means problem"
    - "low rank approximation of binary matrices column subset selection and generalizations"
    - "a simple d 2 sampling based ptas for k means and other clustering problems"

Connected through hyperedge HE-3 (induced by source paper "bregman divergence bounds and universality properties of the logarithmic loss"):
  with members:
    - "on f divergences integral representations local behavior and inequalities"
    - "on the universality of the logistic loss function"
    - "universality of logarithmic loss in lossy compression"
    - "opening the black box of deep neural networks via information"
    - "f divergence inequalities"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"
    - "information measures the curious case of the binary alphabet"

Connected through hyperedge HE-4 (induced by source paper "parameterized low rank binary matrix approximation"):
  with members:
    - "approximation schemes for low rank binary matrix approximation problems"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"
    - "fast biclustering by dual parameterization"
    - "sketching as a tool for numerical linear algebra"
    - "np hardness of hypercube 2 segmentation"

Connected through hyperedge HE-5 (induced by source paper "innovation representation of stochastic processes with application to causal inference"):
  with members:
    - "on the universality of the logistic loss function"
    - "lossless and lossy compression of random forests"
    - "bregman divergence bounds and the universality of the logarithmic loss"
    - "entropic causal inference"
    - "large alphabet source coding using independent component analysis"
    - "learning causal graphs with small interventions"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "on the hardness of entropy minimization and related problems"

Connected through hyperedge HE-6 (induced by source paper "approximation schemes for low rank binary matrix approximation problems"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "parameterized low rank binary matrix approximation"
    - "input sparsity and hardness for robust subspace approximation"
    - "on low rank approximation of binary matrices"
    - "sketching as a tool for numerical linear algebra"
    - "computing a nonnegative matrix factorization provably"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-7 (induced by source paper "lossless and lossy compression of random forests"):
  with members:
    - "bregman divergence bounds and universality properties of the logarithmic loss"
    - "on the universality of the logistic loss function"
    - "deep forest"
    - "deep forest towards an alternative to deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "large alphabet source coding using independent component analysis"):
  with members:
    - "guesswork large deviations and shannon entropy"

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)