human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "detecting path anomalies in time series data on networks"):
  Abstract: The unsupervised detection of anomalies in time series data has important applications, e.g., in user behavioural modelling, fraud detection, and cybersecurity. Anomaly detection has been extensively studied in categorical sequences, however we often have access to time series data that contain paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies we must account for the fact that such data contain a large number of independent observations of short paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem we introduce a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph, which provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.

Includes members:
  Member "from relational data to graphs inferring significant links using generalized hypergeometric ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transforming collaboration data into network layers for enhanced analytics")
  Member "when is a network a network multi order graphical model selection in pathways and temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "honem network embedding using higher order patterns in sequential data")
  Member "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what makes a link successful on wikipedia")
  Member "modelling sequences and temporal networks with dynamic community structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computational human dynamics")
  Member "representing higher order dependencies in networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational human dynamics")
  Member "keeping it short and simple summarising complex event sequences with multivariate patterns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpretable and steerable sequence learning via prototypes")
  Member "hyptrails a bayesian approach for comparing hypotheses about human trails on the web":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploring the intersections of web science and accessibility")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")

Question: Please tell me which class the source paper 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)