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 device positioning and clock synchronization in 5g ultra dense networks"
  Abstract: In this article, we address the prospects and key enabling technologies for highly efficient and accurate device positioning and tracking in 5G radio access networks. Building on the premises of ultra-dense networks as well as on the adoption of multicarrier waveforms and antenna arrays in the access nodes (ANs), we first formulate extended Kalman filter (EKF)-based solutions for computationally efficient joint estimation and tracking of the time of arrival (ToA) and direction of arrival (DoA) of the user nodes (UNs) using uplink reference signals. Then, a second EKF stage is proposed in order to fuse the individual DoA/ToA estimates from one or several ANs into a UN position estimate. Since all the processing takes place at the network side, the computing complexity and energy consumption at the UN side are kept to a minimum. The cascaded EKFs proposed in this article also take into account the unavoidable relative clock offsets between UNs and ANs, such that reliable clock synchronization of the access-link is obtained as a valuable by-product. The proposed cascaded EKF scheme is then revised and extended to more general and challenging scenarios where not only the UNs have clock offsets against the network time, but also the ANs themselves are not mutually synchronized in time. Finally, comprehensive performance evaluations of the proposed solutions on a realistic 5G network setup, building on the METIS project based outdoor Madrid map model together with complete ray tracing based propagation modeling, are provided. The obtained results clearly demonstrate that by using the developed methods, sub-meter scale positioning and tracking accuracy of moving devices is indeed technically feasible in future 5G radio access networks operating at sub-6GHz frequencies, despite the realistic assumptions related to clock offsets and potentially even under unsynchronized network elements.

Connected through hyperedge HE-1 (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"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "tracking angles of departure and arrival in a mobile millimeter wave channel"
    - "fingerprinting based positioning in distributed massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "benefit of joint doa and delay estimation with application to indoor localization in wifi and 5g"):
  with members:
    - "joint 3d positioning and network synchronization in 5g ultra dense networks using ukf and ekf"
    - "high efficiency device positioning and location aware communications in dense 5g networks"

Connected through hyperedge HE-3 (induced by source paper "user positioning in mmw 5g networks using beam rsrp measurements and kalman filtering"):
  with members:
    - "power based direction of arrival estimation using a single multi mode antenna"

Connected through hyperedge HE-4 (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"
    - "cognitive indoor positioning and tracking using multipath channel information"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "fingerprinting based positioning in distributed massive mimo 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-5 (induced by source paper "a bayesian algorithm for distributed network localization using distance and direction data"):
  with members:
    - "cooperative simultaneous localization and synchronization in mobile agent networks"
    - "localization in mobile networks via virtual convex hulls"
    - "cooperative localization for mobile networks a distributed belief propagation mean field message passing algorithm"
    - "a tractable analysis of the improvement in unique localizability through collaboration"
    - "sub nanosecond time of flight on commercial wi fi cards"
    - "loopy belief propagation for approximate inference an empirical study"
    - "convergent message passing algorithms a unifying view"
    - "cooperative and distributed localization for wireless sensor networks in multipath environments"

Connected through hyperedge HE-6 (induced by source paper "joint 3d positioning and network synchronization in 5g ultra dense networks using ukf and ekf"):
  with members:
    - "five disruptive technology directions for 5g"

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)