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: "edgecentric anomaly detection in edge attributed networks"
  Abstract: Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of number of stars, and edges in online social and phonecall networks contain temporal information about when friendships were formed and when users communicated with each other -- in such cases, edge attributes capture information about how the adjacent nodes interact with other entities in the network. In this paper, we aim to utilize exactly this information to discern suspicious from typical node behavior. Our work has a number of notable contributions, including (a) formulation: while most other graph-based anomaly detection works use structural graph connectivity or node information, we focus on the new problem of leveraging edge information, (b) methodology: we introduce EdgeCentric, an intuitive and scalable compression-based approach for detecting edge-attributed graph anomalies, and (c) practicality: we show that EdgeCentric successfully spots numerous such anomalies in several large, edge-attributed real-world graphs, including the Flipkart e-commerce graph with over 3 million product reviews between 1.1 million users and 545 thousand products, where it achieved 0.87 precision over the top 100 results.

Connected through hyperedge HE-1 (induced by source paper "fraud detection in networks state of the art"):
  with members:
    - "unsupervised dictionary learning for anomaly detection"
    - "spotting collective behaviour of online frauds in customer reviews"
    - "deep learning for anomaly detection a survey"
    - "midas microcluster based detector of anomalies in edge streams"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-2 (induced by source paper "silcendice mining suspicious multi attribute entity groups with multi view graphs"):
  with members:
    - "false information on web and social media a survey"
    - "the many faces of link fraud"

Connected through hyperedge HE-3 (induced by source paper "catching loosely synchronized behavior in face of camouflage"):
  with members:
    - "fairjudge trustworthy user prediction in rating platforms"
    - "birdnest bayesian inference for ratings fraud detection"
    - "towards detecting compromised accounts on social networks"
    - "spotting suspicious link behavior with fbox an adversarial perspective"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "community detection via semi synchronous label propagation algorithms"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-4 (induced by source paper "lookout on time evolving graphs succinctly explaining anomalies from any detector"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "deepwalk online learning of social representations"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-5 (induced by source paper "holoscope topology and spike aware fraud detection"):
  with members:
    - "modeling review spam using temporal patterns and co bursting behaviors"
    - "the bursty dynamics of the twitter information network"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-6 (induced by source paper "birdnest bayesian inference for ratings fraud detection"):
  with members:
    - "spotting suspicious link behavior with fbox an adversarial perspective"
    - "finding deceptive opinion spam by any stretch of the imagination"

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)