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: "bayesian discovery of threat networks"
  Abstract: A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utilizes spatio-temporal relationships between vertices, and is used for a specific space-time formulation that leads to significant performance improvements on coordinated covert networks. This performance is demonstrated using a new hybrid mixed-membership blockmodel introduced to simulate random covert networks with realistic properties.

Connected through hyperedge HE-1 (induced by source paper "influence estimation on social media networks using causal inference"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "graph cluster randomization network exposure to multiple universes"

Connected through hyperedge HE-2 (induced by source paper "streaming graph challenge stochastic block partition"):
  with members:
    - "static graph challenge subgraph isomorphism"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "a high performance implementation of spectral clustering on cpu gpu platforms"
    - "an incremental local first community detection method for dynamic graphs"
    - "fast detection of overlapping communities via online tensor methods"
    - "network sampling from static to streaming graphs"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-3 (induced by source paper "the power of side information in subgraph detection"):
  with members:
    - "local set based graph signal reconstruction"
    - "signal recovery on graphs variation minimization"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "phase transitions in spectral community detection"
    - "graph based anomaly detection and description a survey"
    - "defining and evaluating network communities based on ground truth"
    - "generalized optimization framework for graph based semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "a spectral framework for anomalous subgraph detection"):
  with members:
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "spectra of random graphs with arbitrary expected degrees"
    - "graph spectra and the detectability of community structure in networks"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-5 (induced by source paper "measuring global similarity between texts"):
  with members:
    - "network detection theory and performance"
    - "robust multi robot optimal path planning with temporal logic constraints"
    - "optimal multi robot path planning with temporal logic constraints"

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.SI(Social and Information Networks)