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: "a novel framework for modeling and mitigating distributed link flooding attacks"
  Abstract: Distributed link-flooding attacks constitute a new class of attacks with the potential to segment large areas of the Internet. Their distributed nature makes detection and mitigation very hard. This work proposes a novel framework for the analytical modeling and optimal mitigation of such attacks. The detection is modeled as a problem of relational algebra, representing the association of potential attackers (bots) to potential targets. The analysis seeks to optimally dissolve all but the malevolent associations. The framework is implemented at the level of online Traffic Engineering (TE), which is naturally triggered on link-flooding events. The key idea is to continuously re-route traffic in a manner that makes persistent participation to link-flooding events highly improbable for any benign source. Thus, bots are forced to adopt a suspicious behavior to remain effective, revealing their presence. The load-balancing objective of TE is not affected at all. Extensive simulations on various topologies validate our analytical findings.

Connected through hyperedge HE-1 (induced by source paper "active link obfuscation to thwart link flooding attacks for internet of things"):
  with members:
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "on the interplay of link flooding attacks and traffic engineering"

Connected through hyperedge HE-2 (induced by source paper "early detection of crossfire attacks using deep learning"):
  with members:
    - "detecting target area link flooding ddos attacks using traffic analysis and supervised learning"
    - "on the interplay of link flooding attacks and traffic engineering"
    - "learning to hash with binary deep neural network"
    - "deep residual learning for image recognition"
    - "towards defeating the crossfire attack using sdn"

Connected through hyperedge HE-3 (induced by source paper "detecting target area link flooding ddos attacks using traffic analysis and supervised learning"):
  with members:
    - "early detection of crossfire attacks using deep learning"
    - "on the interplay of link flooding attacks and traffic engineering"
    - "towards defeating the crossfire attack using sdn"

Connected through hyperedge HE-4 (induced by source paper "crossfire attack detection using deep learning in software defined its networks"):
  with members:
    - "adam a method for stochastic optimization"
    - "towards defeating the crossfire attack using sdn"

Connected through hyperedge HE-5 (induced by source paper "application network collaboration using sdn for ultra low delay teleorchestras"):
  with members:
    - "on the interplay of link flooding attacks and traffic engineering"
    - "backpressure on the backbone a lightweight non intrusive traffic engineering approach"
    - "a lightweight non intrusive approach for orchestrating autonomously managed network elements"

Connected through hyperedge HE-6 (induced by source paper "the socket store an app model for the application network interaction"):
  with members:
    - "on the interplay of link flooding attacks and traffic engineering"

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)