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: "partially distributed resource allocation in small cell networks"
  Abstract: We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are as follows: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide the results of numerical simulations to illustrate the efficacy of our scheme.

Connected through hyperedge HE-1 (induced by source paper "queue aware joint dynamic interference coordination and heterogeneous qos provisioning in ofdma networks"):
  with members:
    - "throughput optimization in multichannel cognitive radios with hard deadline constraints"
    - "utility optimal dynamic rate allocation under average end to end delay requirements"
    - "decentralized throughput maximizing policies for deadline constrained wireless networks"
    - "resource allocation optimization for delay sensitive traffic in fronthaul constrained cloud radio access networks"
    - "distributed interference management policies for heterogeneous small cell networks"

Connected through hyperedge HE-2 (induced by source paper "intelligent wireless communications enabled by cognitive radio and machine learning"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"
    - "deep learning for wireless physical layer opportunities and challenges"

Connected through hyperedge HE-3 (induced by source paper "analysis of downlink and uplink decoupling in dense cellular networks"):
  with members:
    - "why to decouple the uplink and downlink in cellular networks and how to do it"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "5g ultra dense cellular networks"
    - "joint rate and sinr coverage analysis for decoupled uplink downlink biased cell associations in hetnets"
    - "capacity analysis of decoupled downlink and uplink access in 5g heterogeneous systems"
    - "downlink and uplink decoupling a disruptive architectural design for 5g networks"
    - "five disruptive technology directions for 5g"
    - "analysis of blockage effects on urban cellular networks"

Connected through hyperedge HE-4 (induced by source paper "load aware dynamic spectrum access for small cell networks a graphical game approach"):
  with members:
    - "a game theoretic perspective on self organizing optimization for cognitive small cells"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "full duplex cloud radio access networks an information theoretic viewpoint"
    - "ergodic capacity analysis of remote radio head associations in cloud radio access networks"
    - "coalitional games with overlapping coalitions for interference management in small cell networks"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"

Connected through hyperedge HE-5 (induced by source paper "centralized distributed spectrum access for small cell networks a cloud based game solution"):
  with members:
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "full duplex cloud radio access networks an information theoretic viewpoint"
    - "ergodic capacity analysis of remote radio head associations in cloud radio access networks"
    - "coalitional games with overlapping coalitions for interference management in small cell networks"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access 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)