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: "algebraic signal processing theory"
  Abstract: This paper presents an algebraic theory of linear signal processing. At the core of algebraic signal processing is the concept of a linear signal model defined as a triple (A, M, phi), where familiar concepts like the filter space and the signal space are cast as an algebra A and a module M, respectively, and phi generalizes the concept of the z-transform to bijective linear mappings from a vector space of, e.g., signal samples, into the module M. A signal model provides the structure for a particular linear signal processing application, such as infinite and finite discrete time, or infinite or finite discrete space, or the various forms of multidimensional linear signal processing. As soon as a signal model is chosen, basic ingredients follow, including the associated notions of filtering, spectrum, and Fourier transform. The shift operator is a key concept in the algebraic theory: it is the generator of the algebra of filters A. Once the shift is chosen, a well-defined methodology leads to the associated signal model. Different shifts correspond to infinite and finite time models with associated infinite and finite z-transforms, and to infinite and finite space models with associated infinite and finite C-transforms (that we introduce). In particular, we show that the 16 discrete cosine and sine transforms are Fourier transforms for the finite space models. Other definitions of the shift naturally lead to new signal models and to new transforms as associated Fourier transforms in one and higher dimensions, separable and non-separable. We explain in algebraic terms shift-invariance (the algebra of filters A is commutative), the role of boundary conditions and signal extensions, the connections between linear transforms and linear finite Gauss-Markov fields, and several other concepts and connections.

Connected through hyperedge HE-1 (induced by source paper "discrete signal processing with set functions"):
  with members:
    - "fast iterative combinatorial auctions via bayesian learning"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discrete signal processing on graphs sampling theory"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-2 (induced by source paper "powerset convolutional neural networks"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the limitations of representing functions on sets"
    - "learning a sat solver from single bit supervision"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep sets"
    - "simplicial closure and higher order link prediction"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "neural arithmetic logic units"
    - "quadrature based features for kernel approximation"
    - "learning compressed transforms with low displacement rank"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "strassennets deep learning with a multiplication budget"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a neighborhood preserving translation operator on graphs"):
  with members:
    - "localization bounds for the graph translation"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-5 (induced by source paper "discrete signal processing on graphs sampling theory"):
  with members:
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "local set based graph signal reconstruction"
    - "a probabilistic interpretation of sampling theory of graph signals"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"
    - "learning parametric dictionaries for signals on graphs"
    - "active semi supervised learning using sampling theory for graph signals"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-6 (induced by source paper "signal recovery on graphs variation minimization"):
  with members:
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs frequency analysis"
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "decentralized sparsity regularized rank minimization algorithms and applications"
    - "discrete signal processing on graphs"
    - "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"

Connected through hyperedge HE-7 (induced by source paper "discrete signal processing on graphs frequency analysis"):
  with members:
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"

Connected through hyperedge HE-8 (induced by source paper "discrete signal processing on graphs"):
  with members:
    - "a spectral graph uncertainty principle"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"

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)