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: "fingerprinting based positioning in distributed massive mimo systems"
  Abstract: Location awareness in wireless networks may enable many applications such as emergency services, autonomous driving and geographic routing. Although there are many available positioning techniques, none of them is adapted to work with massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G technology candidate. In this paper, we discuss possible solutions for positioning of mobile stations using a vector of signals at the base station, equipped with many antennas distributed over deployment area. Our main proposal is to use fingerprinting techniques based on a vector of received signal strengths. This kind of methods are able to work in highly-cluttered multipath environments, and require just one base station, in contrast to standard range-based and angle-based techniques. We also provide a solution for fingerprinting-based positioning based on Gaussian process regression, and discuss main applications and challenges.

Connected through hyperedge HE-1 (induced by source paper "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"):
  with members:
    - "channel extrapolation in fdd massive mimo theoretical analysis and numerical validation"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "massive mimo ten myths and one critical question"

Connected through hyperedge HE-2 (induced by source paper "tracking position and orientation through millimeter wave lens mimo in 5g systems"):
  with members:
    - "optimal precoders for tracking the aod and aoa of a mmwave path"
    - "direct localization for massive mimo"
    - "error bounds for uplink and downlink 3d localization in 5g mmwave systems"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "joint device positioning and clock synchronization in 5g ultra dense networks"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "tracking angles of departure and arrival in a mobile millimeter wave channel"

Connected through hyperedge HE-3 (induced by source paper "massive mimo based localization and mapping exploiting phase information of multipath components"):
  with members:
    - "a belief propagation algorithm for multipath based slam"
    - "a scalable belief propagation algorithm for radio signal based slam"
    - "direct localization for massive mimo"
    - "error bounds for uplink and downlink 3d localization in 5g mmwave systems"
    - "position and orientation estimation through millimeter wave mimo in 5g systems"
    - "measurement based wideband analysis of dynamic multipath propagation in vehicular communication scenarios"
    - "evaluation of position related information in multipath components for indoor positioning"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-4 (induced by source paper "channel charting locating users within the radio environment using channel state information"):
  with members:
    - "direct localization for massive mimo"
    - "5g ultra dense cellular networks"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "fundamental limits of cooperation"
    - "cellsense an accurate energy efficient gsm positioning system"

Connected through hyperedge HE-5 (induced by source paper "beyond massive mimo the potential of positioning with large intelligent surfaces"):
  with members:
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "the potential of using large antenna arrays on intelligent surfaces"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "fundamental limits of wideband localization part i a general framework"

Connected through hyperedge HE-6 (induced by source paper "csi based outdoor localization for massive mimo experiments with a learning approach"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "modern wlan fingerprinting indoor positioning methods and deployment challenges"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-7 (induced by source paper "position and orientation estimation through millimeter wave mimo in 5g systems"):
  with members:
    - "direct localization for massive mimo"
    - "error bounds for uplink and downlink 3d localization in 5g mmwave systems"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "joint device positioning and clock synchronization in 5g ultra dense networks"
    - "cognitive indoor positioning and tracking using multipath channel information"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-8 (induced by source paper "direct localization for massive mimo"):
  with members:
    - "an esprit based approach for 2 d localization of incoherently distributed sources in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "emerging technologies and research challenges for 5g wireless networks"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "towards a mathematical theory of super resolution"
    - "collaborative beamforming for distributed wireless ad hoc sensor 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)