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: "small cell offloading through cooperative communication in software defined heterogeneous networks"
  Abstract: To meet the ever-growing demand for a higher communicating rate and better communication quality, more and more small cells are overlaid under the macro base station (MBS) tier, thus forming the heterogeneous networks. Small cells can ease the load pressure of MBS but lack of the guarantee of performance. On the other hand, cooperation draws more and more attention because of the great potential of small cell densification. Some technologies matured in wired network can also be applied to cellular networks, such as software-defined networking (SDN). SDN helps simplify the structure of multi-tier networks. In addition, it is more reasonable for the SDN controller to implement cell coordination. In this paper, we propose a method to offload users from MBSs through small cell cooperation in heterogeneous networks. Association probability is the main indicator of offloading. By using the tools from stochastic geometry, we then obtain the coverage probabilities when users are associated with different types of BSs. All the cell association and cooperation are conducted by the SDN controller. Then, on this basis, we compare the overall coverage probabilities, achievable rate, and energy efficiency with and without cooperation. Numerical results show that small cell cooperation can offload more users from MBS tier. It can also increase the system’s coverage performance. As small cells become denser, cooperation can bring more gains to the energy efficiency of the network.

Connected through hyperedge HE-1 (induced by source paper "the wireless control plane an overview and directions for future research"):
  with members:
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a statistical characterization of localization performance in wireless networks"
    - "deep reinforcement learning a brief survey"
    - "survey of important issues in uav communication networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "achieving ultra low latency in 5g millimeter wave cellular networks"
    - "dueling network architectures for deep reinforcement learning"
    - "learning continuous control policies by stochastic value gradients"

Connected through hyperedge HE-2 (induced by source paper "edge and central cloud computing a perfect pairing for high energy efficiency and low latency"):
  with members:
    - "spectral and energy efficiency of uplink d2d underlaid massive mimo cellular networks"
    - "performance characterization of a real time massive mimo system with los mobile channels"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "user association in 5g networks a survey and an outlook"
    - "energy efficient resource allocation for mobile edge computing based augmented reality applications"
    - "energy efficient resource allocation for mobile edge computation offloading"

Connected through hyperedge HE-3 (induced by source paper "5g converged cell less communications in smart cities"):
  with members:
    - "interference minimization in 5g heterogeneous networks"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "5g ultra dense cellular networks"
    - "5g wireless backhaul networks challenges and research advances"
    - "mobile converged networks framework optimization and challenges"
    - "modeling and analysis of k tier downlink heterogeneous cellular networks"

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)