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: "seeing the unobservable channel learning for wireless communication networks"
  Abstract: Wireless communication networks rely heavily on channel state information (CSI) to make informed decision for signal processing and network operations. However, the traditional CSI acquisition methods is facing many difficulties: pilot-aided channel training consumes a great deal of channel resources and reduces the opportunities for energy saving, while location-aided channel estimation suffers from inaccurate and insufficient location information. In this paper, we propose a novel channel learning framework, which can tackle these difficulties by inferring unobservable CSI from the observable one. We formulate this framework theoretically and illustrate a special case in which the learnability of the unobservable CSI can be guaranteed. Possible applications of channel learning are then described, including cell selection in multi-tier networks, device discovery for device-to-device (D2D) communications, as well as end-to-end user association for load balancing. We also propose a neuron-network-based algorithm for the cell selection problem in multi-tier networks. The performance of this algorithm is evaluated using geometry-based stochastic channel model (GSCM). In settings with 5 small cells, the average cell-selection accuracy is 73% - only a 3.9% loss compared with a location-aided algorithm which requires genuine location information.

Connected through hyperedge HE-1 (induced by source paper "inferring remote channel state information cram er rao lower bound and deep learning implementation"):
  with members:
    - "a new view of multi user hybrid massive mimo non orthogonal angle division multiple access"
    - "how often should csi be updated for massive mimo systems with massive connectivity"
    - "remote channel inference for beamforming in ultra dense hyper cellular network"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "time sequence channel inference for beam alignment in vehicular networks"):
  with members:
    - "remote channel inference for beamforming in ultra dense hyper cellular network"
    - "a critical review of recurrent neural networks for sequence learning"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "a two step learning and interpolation method for location based channel database"):
  with members:
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "remote channel inference for beamforming in ultra dense hyper cellular network"):
  with members:
    - "software defined hyper cellular architecture for green and elastic wireless access"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "what will 5g be"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "coding the beams improving beamforming training in mmwave communication system"

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)