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: "signal processing on graphs causal modeling of un structured data"
  Abstract: Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US. These data are often referred to as  un  structured. The first task in its analytics is to derive a low dimensional representation, a graph or discrete manifold, that describes well the  inter relations among the time series and their  intra  relations across time. This paper presents a computationally tractable algorithm for estimating this graph that structures the data. The resulting graph is directed and weighted, possibly capturing causal relations, not just reciprocal correlations as in many existing approaches in the literature. A convergence analysis is carried out. The algorithm is demonstrated on random graph datasets and real network time series datasets, and its performance is compared to that of related methods. The adjacency matrices estimated with the new method are close to the true graph in the simulated data and consistent with prior physical knowledge in the real dataset tested.

Connected through hyperedge HE-1 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "graph learning under sparsity priors"
    - "blind identification of graph filters"
    - "network inference via the time varying graphical lasso"
    - "graph based transform coding with application to image compression"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"

Connected through hyperedge HE-3 (induced by source paper "network inference from consensus dynamics with unknown parameters"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "graph learning from data under laplacian and structural constraints"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"

Connected through hyperedge HE-4 (induced by source paper "graph learning for spatiotemporal signal with long short term characterization"):
  with members:
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "learning sparse graphs under smoothness prior"
    - "robust principal component analysis on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "topology inference over networks with nonlinear coupling"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "social learning over weakly connected graphs"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "efficiently learning ising models on arbitrary graphs"
    - "directed information graphs"

Connected through hyperedge HE-6 (induced by source paper "local tomography of large networks under the low observability regime"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "network inference from consensus dynamics"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"

Connected through hyperedge HE-7 (induced by source paper "graph learning from filtered signals graph system and diffusion kernel identification"):
  with members:
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "graph learning from data under laplacian and structural constraints"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "robust subspace clustering"
    - "learning laplacian matrix in smooth graph signal representations"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-8 (induced by source paper "plato approximate analytics over compressed time series with tight deterministic error guarantees"):
  with members:
    - "blinkdb queries with bounded errors and bounded response times on very large 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)