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: "detecting criminal organizations in mobile phone networks"
  Abstract: Abstract   The study of criminal networks using traces from heterogeneous communication media is acquiring increasing importance in nowadays society. The usage of communication media such as mobile phones and online social networks leaves digital traces in the form of metadata that can be used for this type of analysis. The goal of this work is twofold: first we provide a theoretical framework for the problem of detecting and characterizing criminal organizations in networks reconstructed from phone call records. Then, we introduce an expert system to support law enforcement agencies in the task of unveiling the underlying structure of criminal networks hidden in communication data. This platform allows for statistical network analysis, community detection and visual exploration of mobile phone network data. It enables forensic investigators to deeply understand hierarchies within criminal organizations, discovering members who play central role and provide connection among sub-groups. Our work concludes illustrating the adoption of our computational framework for a real-word criminal investigation.

Connected through hyperedge HE-1 (induced by source paper "an ensemble based on a bi objective evolutionary spectral algorithm for graph clustering"):
  with members:
    - "spectral redemption in clustering sparse networks"
    - "enhancing community detection using a network weighting strategy"

Connected through hyperedge HE-2 (induced by source paper "detecting problematic transactions in a consumer to consumer e commerce network"):
  with members:
    - "holoscope topology and spike aware fraud detection"
    - "anomaly detection in online social networks"
    - "machine learning for e mail spam filtering review techniques and trends"
    - "graph based anomaly detection and description a survey"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "an expert system for detecting automobile insurance fraud using social network analysis"

Connected through hyperedge HE-3 (induced by source paper "collective classification of spam campaigners on twitter a hierarchical meta path based approach"):
  with members:
    - "poised spotting twitter spam off the beaten paths"
    - "dial one for scam a large scale analysis of technical support scams"
    - "botornot a system to evaluate social bots"
    - "detecting smart spammers on social network a topic model approach"
    - "the rise of social bots"
    - "towards detecting compromised accounts on social networks"
    - "meta path based collective classification in heterogeneous information networks"
    - "hetesim a general framework for relevance measure in heterogeneous networks"

Connected through hyperedge HE-4 (induced by source paper "crime in urban areas a data mining perspective"):
  with members:
    - "deep air learning interpolation prediction and feature analysis of fine grained air quality"
    - "taming near repeat calculation for crime analysis via cohesive subgraph computing"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "social network intelligence analysis to combat street gang violence"

Connected through hyperedge HE-5 (induced by source paper "olcpm an online framework for detecting overlapping communities in dynamic social networks"):
  with members:
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "community detection using a neighborhood strength driven label propagation algorithm"

Connected through hyperedge HE-6 (induced by source paper "network structure and resilience of mafia syndicates"):
  with members:
    - "forensic analysis of phone call networks"
    - "enhancing community detection using a network weighting strategy"
    - "a novel measure of edge centrality in social networks"
    - "the anatomy of the facebook social graph"
    - "crawling facebook for social network analysis purposes"

Connected through hyperedge HE-7 (induced by source paper "from community detection to community deception"):
  with members:
    - "community detection in networks with node attributes"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-8 (induced by source paper "using shortest path to discover criminal community"):
  with members:
    - "anomaly detection in online social networks"
    - "identifying a criminal s network of trust"
    - "detect overlapping and hierarchical community structure in networks"

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)