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: "soft defined heterogeneous vehicular network architecture and challenges"
  Abstract: Heterogeneous Vehicular NETworks (HetVNETs) can meet various quality-of-service (QoS) requirements for intelligent transport system (ITS) services by integrating different access networks coherently. However, the current network architecture for HetVNET cannot efficiently deal with the increasing demands of rapidly changing network landscape. Thanks to the centralization and flexibility of the cloud radio access network (Cloud-RAN), soft-defined networking (SDN) can conveniently be applied to support the dynamic nature of future HetVNET functions and various applications while reducing the operating costs. In this paper, we first propose the multi-layer Cloud RAN architecture for implementing the new network, where the multi-domain resources can be exploited as needed for vehicle users. Then, the high-level design of soft-defined HetVNET is presented in detail. Finally, we briefly discuss key challenges and solutions for this new network, corroborating its feasibility in the emerging fifth-generation (5G) era.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-2 (induced by source paper "design and implementation of location and activity monitoring system based on lora"):
  with members:
    - "x lora an open source lpwa network"
    - "low power wide area networks an overview"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"

Connected through hyperedge HE-3 (induced by source paper "intelligent network slicing for v2x services towards 5g"):
  with members:
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (induced by source paper "mobile big data analysis with machine learning"):
  with members:
    - "decorrelation of neutral vector variables theory and applications"
    - "distributed optimization using the primal dual method of multipliers"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "adversarial network bottleneck features for noise robust speaker verification"
    - "the role of data analysis in the development of intelligent energy networks"
    - "big data caching for networking moving from cloud to edge"
    - "mobile big data analytics using deep learning and apache spark"
    - "5g wireless backhaul networks challenges and research advances"

Connected through hyperedge HE-5 (induced by source paper "wearable affective robot"):
  with members:
    - "joint optimization of computation and communication power in multi user massive mimo systems"
    - "dense small cell networks from noise limited to dense interference limited"
    - "energy aware traffic offloading for green heterogeneous networks"
    - "efficient multi user computation offloading for mobile edge cloud computing"

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)