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: "towards defeating the crossfire attack using sdn"
  Abstract: In this work, we propose online traffic engineering as a novel approach to detect and mitigate an emerging class of stealthy Denial of Service (DoS) link-flooding attacks. Our approach exploits the Software Defined Networking (SDN) paradigm, which renders the management of network traffic more flexible through centralised flow-level control and monitoring. We implement a full prototype of our solution on an emulated SDN environment using OpenFlow to interface with the network devices. We further discuss useful insights gained from our preliminary experiments as well as a number of open research questions which constitute work in progress.

Connected through hyperedge HE-1 (induced by source paper "stackelberg equilibria for two player network routing games on parallel networks"):
  with members:
    - "on the interplay of link flooding attacks and traffic engineering"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

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"
    - "a novel framework for modeling and mitigating distributed link flooding attacks"
    - "learning to hash with binary deep neural network"
    - "deep residual learning for image recognition"

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"
    - "a novel framework for modeling and mitigating distributed link flooding attacks"

Connected through hyperedge HE-4 (induced by source paper "crossfire attack detection using deep learning in software defined its networks"):
  with members:
    - "a novel framework for modeling and mitigating distributed link flooding attacks"
    - "adam a method for stochastic optimization"

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)