human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "friendship and selfishness forwarding applying machine learning techniques to opportunistic networks data forwarding"):
  Abstract: Opportunistic networks could become the solution to provide communication support in both cities where the cellular network could be overloaded, and in scenarios where a fixed infrastructure is not available, like in remote and developing regions. A critical issue that still requires a satisfactory solution is the design of an efficient data delivery solution. Social characteristics are recently being considered as a promising alternative. Most opportunistic network applications rely on the different mobile devices carried by users, and whose behavior affects the use of the device itself. #R##N#This work presents the "Friendship and Selfishness Forwarding" (FSF) algorithm. FSF analyses two aspects to make message forwarding decisions when a contact opportunity arises: First, it classifies the friendship strength among a pair of nodes by using a machine learning algorithm to quantify the friendship strength among pairs of nodes in the network. Next, FSF assesses the relay node selfishness to consider those cases in which, despite a strong friendship with the destination, the relay node may not accept to receive the message because it is behaving selfishly, or because its device has resource constraints in that moment. #R##N#By using trace-driven simulations through the ONE simulator, we show that the FSF algorithm outperforms previously proposed schemes in terms of delivery rate, average cost, and efficiency.

Includes members:
  Member "optimal forwarding in opportunistic delay tolerant networks with meeting rate estimations":
    also connected through other hyperedges:
      (none)
  Member "socially aware networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "socially aware congestion control in ad hoc networks current status and the way forward")
  Member "nfcu a new friendship based routing with buffer management in opportunistic networks":
    also connected through other hyperedges:
      (none)
  Member "thumbs up sentiment classification using machine learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "description based text classification with reinforcement learning")

Question: Please tell me which class the source paper 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)