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 modeling causal relations in big data"
  Abstract: Many big data 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. A first task in the analytics of these data is to derive a low dimensional representation, a graph or discrete manifold, that describes well the interrelations among the time series and their intrarelations across time. This paper presents a computationally tractable algorithm for estimating this graph structure from the available data. This graph is directed and weighted, possibly representing causation relations, not just correlations as in most existing approaches in the literature. The algorithm is demonstrated on random graph 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 "active sensing of social networks"):
  with members:
    - "the social system identification problem"
    - "analysis and control of beliefs in social networks"
    - "graph theoretic distributed inference in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "social structure of facebook networks"
    - "a unique nonnegative solution to an underdetermined system from vectors to matrices"
    - "higher dimensional consensus learning in large scale networks"

Connected through hyperedge HE-2 (induced by source paper "towards stationary time vertex signal processing"):
  with members:
    - "autoregressive moving average graph filtering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "frequency analysis of temporal graph signals"
    - "stationary time vertex signal processing"
    - "gspbox a toolbox for signal processing on graphs"
    - "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-3 (induced by source paper "network topology identification from spectral templates"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "distributed linear network operators using graph filters"
    - "learning laplacian matrix in smooth graph signal representations"
    - "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"

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)