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: "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
  Abstract: In this letter, we investigate the problem of providing gigabit wireless access with reliable communication in 5G millimeter-wave (mmWave) massive multiple-input multiple-output networks. In contrast to the classical network design based on average metrics, we propose a distributed risk-sensitive reinforcement learning-based framework to jointly optimize the beamwidth and transmit power, while taking into account the sensitivity of mmWave links due to blockage. Numerical results show that our proposed algorithm achieves more than 9 Gbps of user throughput with a guaranteed probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More importantly, there exists a rate-reliability-network density tradeoff, in which as the user density increases from 16 to 96 per km2, the fraction of users that achieves 4 Gbps is reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively.

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"
    - "on device federated learning via blockchain and its latency analysis"
    - "mmwave beam prediction with situational awareness a machine learning approach"

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"
    - "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"
    - "ultra reliable and low latency communication in mmwave enabled massive mimo networks"

Connected through hyperedge HE-3 (induced by source paper "energy efficient power control of train ground mmwave communication for high speed trains"):
  with members:
    - "device to device communications enabled energy efficient multicast scheduling in mmwave small cells"
    - "positioning of high speed trains using 5g new radio synchronization signals"
    - "joint power allocation and beamforming for non orthogonal multiple access noma in 5g millimeter wave communications"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "boosting spatial reuse via multiple path multihop scheduling for directional mmwave wpans"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "coding the beams improving beamforming training in mmwave communication system"

Connected through hyperedge HE-4 (induced by source paper "ultra reliable and low latency wireless communication tail risk and scale"):
  with members:
    - "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"
    - "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"

Connected through hyperedge HE-5 (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"
    - "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"
    - "millimeter wave v2v communications distributed association and beam alignment"

Connected through hyperedge HE-6 (induced by source paper "risk sensitive reinforcement learning for urllc traffic in wireless networks"):
  with members:
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "risk sensitive reinforcement learning applied to control under constraints"

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)