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: "spatial wireless channel prediction under location uncertainty"
  Abstract: Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel.

Connected through hyperedge HE-1 (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-2 (induced by source paper "single anchor two way localization bounds for 5g mmwave systems two protocols"):
  with members:
    - "error bounds for uplink and downlink 3d localization in 5g mmwave systems"
    - "position and orientation estimation through millimeter wave mimo in 5g systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "mitigating pilot contamination through location aware pilot assignment in massive mimo networks"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "what will 5g be"
    - "fundamental limits of wideband localization part ii cooperative networks"
    - "fundamental limits of wideband localization part i a general framework"

Connected through hyperedge HE-3 (induced by source paper "error bounds for uplink and downlink 3d localization in 5g mmwave systems"):
  with members:
    - "performance limits and geometric properties of array localization"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "mitigating pilot contamination through location aware pilot assignment in massive mimo networks"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "evaluation of position related information in multipath components for indoor positioning"
    - "what will 5g be"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "fundamental limits of wideband localization part ii cooperative networks"

Connected through hyperedge HE-4 (induced by source paper "rcamp a resilient communication aware motion planner for mobile robots with autonomous repair of wireless connectivity"):
  with members:
    - "joint communication motion planning in wireless connected robotic networks overview and design guidelines"

Connected through hyperedge HE-5 (induced by source paper "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"):
  with members:
    - "context aware scheduling of joint millimeter wave and microwave resources for dual mode base stations"
    - "anticipatory radio resource management for mobile video streaming with linear programming"
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "on characterization of feasible interference regions in cognitive radio networks"
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"
    - "5g cellular key enabling technologies and research challenges"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "anticipatory mobile computing a survey of the state of the art and research challenges"

Connected through hyperedge HE-6 (induced by source paper "anticipatory networking in future generation mobile networks a survey"):
  with members:
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "predictive green wireless access exploiting mobility and application information"
    - "rate adaptation and admission control for video transmission with subjective quality constraints"
    - "predicting a user s next cell with supervised learning based on channel states"
    - "a comparative study of collaborative filtering algorithms"
    - "femtocaching wireless video content delivery through distributed caching helpers"
    - "mobility prediction in wireless ad hoc networks using neural networks"

Connected through hyperedge HE-7 (induced by source paper "spatial prediction under location uncertainty in cellular networks"):
  with members:
    - "fixed rank kriging for cellular coverage analysis"

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)