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: "amc model for denial of sleep attack detection"
  Abstract: Due to deployment in hostile environment, wireless sensor network is vulnerable to various attacks. Exhausted sensor nodes in sensor network become a challenging issue because it disrupts the normal connectivity of the network. Affected nodes give rise to denial of service that resists to get the objective of sensor network in real life. A mathematical model based on Absorbing Markov Chain (AMC)is proposed for Denial of Sleep attack detection in sensor network. In this mechanism, whether sensor network is affected by denial of sleep attack or not can be decided by considering expected death time of sensor network under normal scenario.

Connected through hyperedge HE-1 (induced by source paper "a novel countermeasure technique to protect wsn against denial of sleep attacks using firefly and hopfield neural network hnn algorithms"):
  with members:
    - "securing of unmanned aerial systems uas against security threats using human immune system"
    - "an artificial immune based approach for detection and isolation misbehavior attacks in wireless networks"
    - "an efficient routing protocol in mobile ad hoc networks by using artificial immune system"
    - "a new approach for improvement security against dos attacks in vehicular ad hoc network"
    - "sleep deprivation attack detection in wireless sensor network"
    - "lightweight hierarchical model for hwsnet"

Connected through hyperedge HE-2 (induced by source paper "securing wireless sensor networks against denial of sleep attacks using rsa cryptography algorithm and interlock protocol"):
  with members:
    - "securing of unmanned aerial systems uas against security threats using human immune system"
    - "a novel countermeasure technique to protect wsn against denial of sleep attacks using firefly and hopfield neural network hnn algorithms"
    - "an artificial immune based approach for detection and isolation misbehavior attacks in wireless networks"
    - "a new approach for improvement security against dos attacks in vehicular ad hoc network"
    - "lightweight hierarchical model for hwsnet"

Connected through hyperedge HE-3 (induced by source paper "segnet secure geo sensor network model"):
  with members:
    - "sleep deprivation attack detection in wireless sensor network"

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)