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: "on the degrees of freedom of signals on graphs"
  Abstract: Continuous-time signals are well known for not being perfectly localized in both time and frequency domains. Conversely, a signal defined over the vertices of a graph can be perfectly localized in both vertex and frequency domains. We derive the conditions ensuring the validity of this property and then, building on this theory, we provide the conditions for perfect reconstruction of a graph signal from its samples. Next, we provide a finite step algorithm for the reconstruction of a band-limited signal from its samples and then we show the effect of sampling a non perfectly band-limited signal and show how to select the bandwidth that minimizes the mean square reconstruction error.

Connected through hyperedge HE-1 (induced by source paper "distributed adaptive learning of graph signals"):
  with members:
    - "multitask diffusion adaptation over networks with common latent representations"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-2 (induced by source paper "adaptive least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"

Connected through hyperedge HE-3 (induced by source paper "least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"
    - "distributed detection and estimation in wireless sensor networks"

Connected through hyperedge HE-4 (induced by source paper "signals on graphs uncertainty principle and sampling"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "toward an uncertainty principle for weighted graphs"
    - "uncertainty principle and sampling of signals defined on graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "near optimal sensor placement for linear inverse problems"
    - "discrete signal processing on graphs frequency analysis"
    - "a spectral graph uncertainty principle"

Connected through hyperedge HE-5 (induced by source paper "convolutional neural networks on graphs with fast localized spectral filtering"):
  with members:
    - "toward an uncertainty principle for weighted graphs"
    - "deep convolutional networks on graph structured data"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "unsupervised deep haar scattering on graphs"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "uncertainty principle and sampling of signals defined on graphs"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "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"

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)