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 "performance evaluation and optimization of b a t m a n v routing for aerial and ground based mobile ad hoc networks"):
  Abstract: The provision of reliable and efficient end-to-end communication within ground- and air-based mobile mesh networks is a major challenge for routing protocols due to the mobility-related dynamics of the channel properties and the resulting mesh network topology. In this paper, we evaluate the performance of the novel Better Approach To Mobile Adhoc Networking (B.A.T.M.A.N.) V routing protocol for vehicular mesh networks and propose a mobility-predictive extension that explicitly addresses highly dynamic communication networks. In order to enable large-scale simulative analysis, we present an open source simulation model, which is validated by field experiments. Within a comprehensive evaluation campaign in Vehicle-to-Everything (V2X) and Unmanned Aerial Vehicle (UAV) scenarios, it is shown that the predictive B.A.T.M.A.N. V-based approach is significantly better suited for maintaining reliable connectivity within highly mobile mesh networks than established routing protocols.

Includes members:
  Member "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer")
  Member "lightweight joint simulation of vehicular mobility and communication with limosim":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks")
  Member "a simple scheme for distributed passive load balancing in mobile ad hoc networks":
    also connected through other hyperedges:
      (none)
  Member "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks")
  Member "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")

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)