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: "delay optimal data forwarding in vehicular sensor networks"
  Abstract: Vehicular Sensor Network (VSN) is emerging as a new solution for monitoring urban environments such as Intelligent Transportation Systems and air pollution. One of the crucial factors that determine the service quality of urban monitoring applications is the delivery delay of sensing data packets in the VSN. In this paper, we study the problem of routing data packets with minimum delay in the VSN, by exploiting i) vehicle traffic statistics, ii) anycast routing and iii) knowledge of future trajectories of vehicles such as buses. We first introduce a novel road network graph model that incorporates the three factors into the routing metric. We then characterize the packet delay on each edge as a function of the vehicle density, speed and the length of the edge. Based on the network model and delay function, we formulate the packet routing problem as a Markov Decision Process (MDP) and develop an optimal routing policy by solving the MDP. Evaluations using real vehicle traces in a city show that our routing policy significantly improves the delay performance compared to existing routing protocols.

Connected through hyperedge HE-1 (induced by source paper "multihop routing for data delivery in v2x networks"):
  with members:
    - "throughput of infrastructure based cooperative vehicular networks"

Connected through hyperedge HE-2 (induced by source paper "joint beamforming and power splitting control in downlink cooperative swipt noma systems"):
  with members:
    - "a minorization maximization method for optimizing sum rate in the downlink of non orthogonal multiple access systems"
    - "cooperative non orthogonal multiple access with simultaneous wireless information and power transfer"
    - "the application of mimo to non orthogonal multiple access"
    - "joint beamforming design and time allocation for wireless powered communication networks"
    - "wireless information and energy transfer in multi antenna interference channel"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "coordinated beamforming for multiuser miso interference channel under rate outage constraints"
    - "relaying protocols for wireless energy harvesting and information processing"

Connected through hyperedge HE-3 (induced by source paper "multi hop links quality analysis of 5g enabled vehicular networks"):
  with members:
    - "5g software defined vehicular networks"
    - "vehicular communications for 5g cooperative small cell networks"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "5g ultra dense cellular networks"

Connected through hyperedge HE-4 (induced by source paper "markov decision processes with applications in wireless sensor networks a survey"):
  with members:
    - "distributed and centralized hybrid csma ca tdma schemes for single hop wireless networks"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "markov decision processes with multiple long run averageobjectives"
    - "on the complexity of solving markov decision problems"
    - "internet of things iot a vision architectural elements and future directions"
    - "sensor scheduling for energy efficient target tracking in sensor networks"
    - "sleep control for tracking in sensor networks"
    - "one counter markov decision processes"

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)