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: "millimeter wave v2v communications distributed association and beam alignment"
  Abstract: Recently, millimeter-wave (mmWave) bands have been postulated as a means to accommodate the foreseen extreme bandwidth demands in vehicular communications, which result from the dissemination of sensory data to nearby vehicles for enhanced environmental awareness and improved safety level. However, the literature is particularly scarce in regards to principled resource allocation schemes that deal with the challenging radio conditions posed by the high mobility of vehicular scenarios. In this paper, we propose a novel framework that blends together matching theory and swarm intelligence to dynamically and efficiently pair vehicles and optimize both transmission and reception beamwidths. This is done by jointly considering channel state information and queue state information when establishing vehicle-to-vehicle (V2V) links. To validate the proposed framework, simulation results are presented and discussed, where the throughput performance as well as the latency/reliability tradeoffs of the proposed approach are assessed and compared with several baseline approaches recently proposed in the literature. The results obtained in this paper show performance gains of 25% in reliability and delay for ultra-dense vehicular scenarios with 50% more active V2V links than the baselines. These results shed light on the operational limits and practical feasibility of mmWave bands, as a viable radio access solution for future high-rate V2V communications.

Connected through hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "how reliable and capable is multi connectivity"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "on device federated learning via blockchain and its latency analysis"

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"
    - "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas"
    - "joint load balancing and interference mitigation in 5g heterogeneous networks"
    - "ultra reliable and low latency communication in mmwave enabled massive mimo networks"

Connected through hyperedge HE-3 (induced by source paper "computation offloading and content caching delivery in vehicular edge computing a survey"):
  with members:
    - "spectrum resource management for multi access edge computing in autonomous vehicular networks"
    - "adaptive learning based task offloading for vehicular edge computing systems"
    - "concurrent transmission scheduling for perceptual data sharing in mmwave vehicular networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "vehicular edge computing via deep reinforcement learning"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
    - "joint offloading and resource allocation in vehicular edge computing and networks"

Connected through hyperedge HE-4 (induced by source paper "rf assisted free space optics for 5g vehicle to vehicle communications"):
  with members:
    - "optical communication in space challenges and mitigation techniques"
    - "millimeter wave vehicular communication to support massive automotive sensing"

Connected through hyperedge HE-5 (induced by source paper "concurrent transmission scheduling for perceptual data sharing in mmwave vehicular networks"):
  with members:
    - "millimeter wave vehicular communication to support massive automotive sensing"

Connected through hyperedge HE-6 (induced by source paper "dynamic task offloading and resource allocation for ultra reliable low latency edge computing"):
  with members:
    - "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 networks convergence of computing caching and communications"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "proactive edge computing in latency constrained fog networks"
    - "a survey on mobile edge computing the communication perspective"
    - "an online secretary framework for fog network formation with minimal latency"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"

Connected through hyperedge HE-7 (induced by source paper "taming the latency in multi user vr 360 circ a qoe aware deep learning aided multicast framework"):
  with members:
    - "had you looked where i m looking cross user similarities in viewing behavior for 360 video and caching implications"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "toward low latency and ultra reliable virtual reality"
    - "beyond wysiwyg sharing contextual sensing data through mmwave v2v communications"

Connected through hyperedge HE-8 (induced by source paper "ultra reliable low latency vehicular networks taming the age of information tail"):
  with members:
    - "average peak age of information minimization in uav assisted iot networks"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "fine grained vs average reliability for v2v communications around intersections"
    - "towards low latency and ultra reliable vehicle to vehicle communication"
    - "latency and reliability aware task offloading and resource allocation for mobile edge computing"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

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)