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: "optimizing space air ground integrated networks by artificial intelligence"
  Abstract: It is widely acknowledged that the development of traditional terrestrial communication technologies cannot provide all users with fair and high quality services due to the scarce network resource and limited coverage areas. To complement the terrestrial connection, especially for users in rural, disaster-stricken, or other difficult-to-serve areas, satellites, unmanned aerial vehicles (UAVs), and balloons have been utilized to relay the communication signals. On the basis, Space-Air-Ground Integrated Networks (SAGINs) have been proposed to improve the users' Quality of Experience (QoE). However, compared with existing networks such as ad hoc networks and cellular networks, the SAGINs are much more complex due to the various characteristics of three network segments. To improve the performance of SAGINs, researchers are facing many unprecedented challenges. In this paper, we propose the Artificial Intelligence (AI) technique to optimize the SAGINs, as the AI technique has shown its predominant advantages in many applications. We first analyze several main challenges of SAGINs and explain how these problems can be solved by AI. Then, we consider the satellite traffic balance as an example and propose a deep learning based method to improve the traffic control performance. Simulation results evaluate that the deep learning technique can be an efficient tool to improve the performance of SAGINs.

Connected through hyperedge HE-1 (induced by source paper "creating efficient blockchains for the internet of things by coordinated satellite terrestrial networks"):
  with members:
    - "blockchain a distributed solution to automotive security and privacy"
    - "bitcoin ng a scalable blockchain protocol"

Connected through hyperedge HE-2 (induced by source paper "the roadmap to 6g ai empowered wireless networks"):
  with members:
    - "6g the next frontier"
    - "a speculative study on 6g"
    - "a hardware efficient analog network structure for hybrid precoding in millimeter wave systems"
    - "learning to communicate in uav aided wireless networks map based approaches"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "data shuffling in wireless distributed computing via low rank optimization"
    - "propagation measurement system and approach at 140 ghz moving to 6g and above 100 ghz"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-3 (induced by source paper "aeronautical ad hoc networking for the internet above the clouds"):
  with members:
    - "regularized zero forcing precoding aided adaptive coding and modulation for large scale antenna array based air to air communications"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "optical communication in space challenges and mitigation techniques"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "survey of important issues in uav communication networks"
    - "on the security of the automatic dependent surveillance broadcast protocol"
    - "security challenges in mobile ad hoc networks a survey"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence enabled intelligent 6g networks"):
  with members:
    - "the roadmap to 6g ai empowered wireless networks"
    - "big data analytics machine learning and artificial intelligence in next generation wireless networks"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"

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)