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 countermeasure technique to protect wsn against denial of sleep attacks using firefly and hopfield neural network hnn algorithms"
  Abstract: Wireless sensor networks (WSNs) contain numerous nodes that their main goals are to monitor and control environments. Also, sensor nodes distribute based on network usage. One of the most significant issues in this type of network is the energy consumption of sensor nodes. In fixed-sink networks, nodes which are near the sink act as an interface to transfer data of other nodes to sink. This causes the energy consumption of sensors reduces rapidly. Therefore, the lifetime of the network declines. Sensor nodes owing to their weaknesses are susceptible to several threats, one of which is denial-of-sleep attack (DoSA) threatening WSN. Hence, the DoSA refers to the energy loss in these nodes by maintaining the nodes from entering energy-saving and sleep mode. In this paper, a hybrid approach is proposed based on mobile sink, firefly algorithm based on leach, and Hopfield neural network (WSN-FAHN). Thus, mobile sink is applied to both improve energy consumption and increase network lifetime. Firefly algorithm is proposed to cluster nodes and authenticate in two levels to prevent from DoSA. In addition, Hopfield neural network detects the direction route of the sink movement to send data of CH. Furthermore, here WSN-FAHN technique is assessed through wide simulations performed in the NS-2 environment. The WSN-FAHN procedure superiority is demonstrated by simulation outcomes in comparison with contemporary schemes based on performance metrics like packet delivery ratio (PDR), average throughput, detection ratio, and network lifetime while decreasing the average residual energy.

Connected through hyperedge HE-1 (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"
    - "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"
    - "amc model for denial of sleep attack detection"
    - "lightweight hierarchical model for hwsnet"

Connected through hyperedge HE-2 (induced by source paper "securing of unmanned aerial systems uas against security threats using human immune system"):
  with members:
    - "multi level trust based intelligence schema for securing of internet of things iot against security threats using cryptographic authentication"
    - "defending against intrusion of malicious uavs with networked uav defense swarms"
    - "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"

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.DC(Distributed, Parallel, and Cluster Computing)