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: "nhad neuro fuzzy based horizontal anomaly detection in online social networks"
  Abstract: Use of social network is the basic functionality of today's life. With the advent of more and more online social media, the information available and its utilization have come under the threat of several anomalies. Anomalies are the major cause of online frauds which allow information access by unauthorized users as well as information forging. One of the anomalies that act as a silent attacker is the horizontal anomaly. These are the anomalies caused by a user because of his/her variable behavior towards different sources. Horizontal anomalies are difficult to detect and hazardous for any network. In this paper, a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery, and removal of horizontal anomalies efficiently and accurately. The proposed approach operates over the five paradigms, namely, missing links, reputation gain, significant difference, trust properties, and trust score. The proposed approach is evaluated with three datasets: DARPA'98 benchmark dataset, synthetic dataset, and real-time traffic. Results show that the accuracy of the proposed NHAD model for 10 to 30 percent anomalies in synthetic dataset ranges between 98.08 and 99.88 percent. The evaluation over DARPA'98 dataset demonstrates that the proposed approach is better than the existing solutions as it provides 99.97 percent detection rate for anomalous class. For real-time traffic, the proposed NHAD model operates with an average accuracy of 99.42 at 99.90 percent detection rate.

Connected through hyperedge HE-1 (induced by source paper "a survey on the security and the evolution of osmotic and catalytic computing for 5g networks"):
  with members:
    - "5g nr jamming spoofing and sniffing threat assessment and mitigation"
    - "a framework for mitigating zero day attacks in iot"
    - "multi radio 5g architecture for connected and autonomous vehicles application and design insights"
    - "rehand secure region based fast handover with user anonymity for small cell networks in 5g"
    - "robust physical layer security for power domain non orthogonal multiple access based hetnets and hudns sic avoidance at eavesdroppers"
    - "a survey on the security of pervasive online social networks posns"
    - "a survey of physical layer security techniques for 5g wireless networks and challenges ahead"
    - "self enforcing game theory based resource allocation for lorawan assisted public safety communications"

Connected through hyperedge HE-2 (induced by source paper "security privacy and trust for smart mobile internet of things m iot a survey"):
  with members:
    - "a framework for mitigating zero day attacks in iot"
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "advertising in the iot era vision and challenges"
    - "managing service heterogeneity using osmotic computing"
    - "extension of mih to support fpmipv6 for optimized heterogeneous handover"
    - "securing wireless communications of the internet of things from the physical layer an overview"
    - "traffic aware patching for cyber security in mobile iot"
    - "trifecta security energy efficiency and communication capacity comparison for wireless iot devices"

Connected through hyperedge HE-3 (induced by source paper "a survey on the security of pervasive online social networks posns"):
  with members:
    - "a framework for mitigating zero day attacks in iot"
    - "self enforcing game theory based resource allocation for lorawan assisted public safety communications"
    - "managing service heterogeneity using osmotic computing"
    - "netspam a network based spam detection framework for reviews in online social media"
    - "a privacy preserving community based p2p osns using broadcast encryption supporting recommendation mechanism"
    - "extension of mih to support fpmipv6 for optimized heterogeneous handover"
    - "analyzing resilience of interest based social networks against node and link failures"
    - "anomaly detection in online social 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.NI(Networking and Internet Architecture)