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: "joint millimeter wave and microwave resources allocation in cellular networks with dual mode base stations"
  Abstract: In this paper, a novel dual-mode scheduling framework is proposed that jointly performs user applications (UA) selection and scheduling over microwave ($\mu$W) and millimeter wave (mmW) bands. The proposed scheduling framework utilizes a set of context information, including the channel state information, the delay tolerance and required load per UA, and the uncertainty of mmW channels, to maximize the quality-of-service (QoS) per UA. The scheduling problem is formulated as an optimization with minimum unsatisfied relations (min-UR) problem which is shown to be challenging to solve. Consequently, a long-term scheduling framework, consisting of two stages, is proposed. Within this framework, first, the scheduling over $\mu$W band is formulated as a matching game and to solve this problem, a novel algorithm is proposed and shown to yield a two-sided stable resource allocation. Second, over the mmW band, the scheduling problem is formulated as a 0-1 Knapsack problem and a novel algorithm is proposed to solve it. Furthermore, it is shown that the proposed framework can find an effective scheduling solution, over both $\mu$W and mmW, in polynomial time. Simulation results show that, compared with conventional scheduling schemes, the proposed approach significantly increases the number of satisfied UAs and enhances the users' quality-of-experience.

Connected through hyperedge HE-1 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning and gaussian process based band assignment in dual band systems"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "modeling and analyzing millimeter wave cellular systems"

Connected through hyperedge HE-2 (induced by source paper "joint access and backhaul resource management in satellite drone networks a competitive market approach"):
  with members:
    - "exploiting noma for uav communications in large scale cellular networks"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "backhaul aware placement of a uav bs with bandwidth allocation for user centric operation and profit maximization"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "beam tracking for uav mounted satcom on the move with massive antenna array"
    - "user association and bandwidth allocation for terrestrial and aerial base stations with backhaul considerations"

Connected through hyperedge HE-3 (induced by source paper "deep learning and gaussian process based band assignment in dual band systems"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "classification algorithms for semi blind uplink downlink decoupling in sub 6 ghz mmwave 5g networks"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "an introduction to deep learning for the physical layer"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "remote channel inference for beamforming in ultra dense hyper cellular network"

Connected through hyperedge HE-4 (induced by source paper "deep learning predictive band switching in wireless networks"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "beam codebook design for 5g mmwave terminals"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"
    - "millimeter wave beam selection using out of band spatial information"

Connected through hyperedge HE-5 (induced by source paper "dual band fading multiple access relay channels"):
  with members:
    - "gaussian 1 2 1 networks capacity results for mmwave communications"
    - "secure communication over 1 2 1 networks"
    - "beam management for millimeter wave beamspace mu mimo systems"
    - "millimeter wave beam selection using out of band spatial information"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "link regime and power savings of decode forward relaying in fading channels"
    - "hybrid millimeter wave systems a novel paradigm for hetnets"

Connected through hyperedge HE-6 (induced by source paper "joint millimeter wave and microwave wave resource allocation design for dual mode base stations"):
  with members:
    - "secure transmission with large numbers of antennas and finite alphabet inputs"
    - "secure massive mimo transmission with an active eavesdropper"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-7 (induced by source paper "end to end simulation of 5g mmwave networks"):
  with members:
    - "integration of carrier aggregation and dual connectivity for the ns 3 mmwave module"
    - "x tcp a cross layer approach for tcp uplink flows in mmwave networks"
    - "enhancing tcp end to end performance in millimeter wave communications"
    - "beam alignment for millimetre wave links with motion prediction of autonomous vehicles"
    - "tcp in 5g mmwave networks link level retransmissions and mp tcp"
    - "provisioning low latency resilient mobile edge clouds for 5g"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "reliable video streaming over mmwave with multi connectivity and network coding"

Connected through hyperedge HE-8 (induced by source paper "federated echo state learning for minimizing breaks in presence in wireless virtual reality networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "towards federated learning at scale system design"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "learning to communicate in uav aided wireless networks map based approaches"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "analysis of memory capacity for deep echo state 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.IT(Information Theory)