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: "backhaul aware interference management in the uplink of wireless small cell networks"
  Abstract: The design of distributed mechanisms for interference management is one of the key challenges in emerging wireless small cell networks whose backhaul is capacity limited and heterogeneous (wired, wireless and a mix thereof). In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed. The proposed approach enables macrocell user equipments (MUEs) to optimize their uplink performance, by exploiting the presence of neighboring small cell base stations. The problem is formulated as a noncooperative game among the MUEs that seek to optimize their delay-rate tradeoff, given the conditions of both the radio access network and the - possibly heterogeneous - backhaul. To solve this game, a novel, distributed learning algorithm is proposed using which the MUEs autonomously choose their optimal uplink transmission strategies, given a limited amount of available information. The convergence of the proposed algorithm is shown and its properties are studied. Simulation results show that, under various types of backhauls, the proposed approach yields significant performance gains, in terms of both average throughput and delay for the MUEs, when compared to existing benchmark algorithms.

Connected through hyperedge HE-1 (induced by source paper "risk sensitive task fetching and offloading for vehicular edge computing"):
  with members:
    - "application of machine learning in wireless networks key techniques and open issues"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-2 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks"):
  with members:
    - "path selection and rate allocation in self backhauled mmwave networks"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "toward low latency and ultra reliable virtual reality"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas"
    - "joint load balancing and interference mitigation in 5g heterogeneous networks"

Connected through hyperedge HE-3 (induced by source paper "application of machine learning in wireless networks key techniques and open issues"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep learning for radio resource allocation in multi cell networks"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-4 (induced by source paper "ultra reliable and low latency wireless communication tail risk and scale"):
  with members:
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "network slicing for ultra reliable low latency communication in industry 4 0 scenarios"
    - "proactive edge computing in latency constrained fog networks"
    - "fine grained reliability for v2v communications around suburban and urban intersections"
    - "introduction to ultra reliable and low latency communications in 5g"

Connected through hyperedge HE-5 (induced by source paper "path selection and rate allocation in self backhauled mmwave networks"):
  with members:
    - "joint load balancing and interference mitigation in 5g heterogeneous networks"
    - "ultra reliable and low latency communication in mmwave enabled massive mimo networks"
    - "tracking mm wave channel dynamics fast beam training strategies under mobility"
    - "joint in band backhauling and interference mitigation in 5g heterogeneous networks"
    - "on the joint impact of beamwidth and orientation error on throughput in directional wireless poisson networks"
    - "what will 5g be"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "quality of service provisioning in decentralized networks a satisfaction equilibrium approach"

Connected through hyperedge HE-6 (induced by source paper "fronthaul aware software defined wireless networks resource allocation and user scheduling"):
  with members:
    - "fronthaul aware software defined joint resource allocation and user scheduling for 5g networks"
    - "programming flows in dense mobile environments a multi user diversity perspective"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "software defined and virtualized future mobile and wireless networks a survey"
    - "a lyapunov optimization approach to repeated stochastic games"

Connected through hyperedge HE-7 (induced by source paper "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"):
  with members:
    - "a multi game framework for harmonized lte u and wifi coexistence over unlicensed bands"
    - "opportunistic sleep mode strategies in wireless small cell networks"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"
    - "holistic small cell traffic balancing across licensed and unlicensed bands"
    - "modeling and analyzing the coexistence of wi fi and lte in unlicensed spectrum"
    - "dynamic clustering and on off strategies for wireless small cell networks"
    - "why to decouple the uplink and downlink in cellular networks and how to do it"
    - "small cell traffic balancing over licensed and unlicensed bands"

Connected through hyperedge HE-8 (induced by source paper "joint downlink cell association and bandwidth allocation for wireless backhauling in two tier hetnets with large scale antenna arrays"):
  with members:
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"
    - "interference management in 5g reverse tdd hetnets with wireless backhaul a large system analysis"
    - "an overview of load balancing in hetnets old myths and open problems"
    - "wireless backhaul networks capacity bound scalability analysis and design guidelines"
    - "unified and distributed qos driven cell association algorithms in heterogeneous networks"
    - "what will 5g be"
    - "massive mimo meets hetnet interference coordination through spatial blanking"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"

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)