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: "two timescale hybrid compression and forward for massive mimo aided c ran"
  Abstract: We consider the uplink of a cloud radio access network (C-RAN), where massive multiple-input multiple-output (MIMO) remote radio heads (RRHs) serve as relays between users and a centralized baseband unit (BBU). Although employing massive MIMO at RRHs can improve the spectral efficiency, it also significantly increases the amount of data that need to be transported over the fronthaul links between RRHs and BBU. Existing fronthaul compression methods for conventional C-RAN are not suitable in the massive MIMO regime because they require fully digital processing and/or real-time full channel state information (CSI), incurring high implementation cost for massive MIMO RRHs. To overcome this challenge, we propose to perform a two-timescale hybrid analog-and-digital spatial filtering at each RRH to reduce the fronthaul consumption. Specifically, the analog filter is adapted to the channel statistics to achieve the massive MIMO array gain, and the digital filter is adapted to the instantaneous effective CSI to achieve the spatial multiplexing gain. Such a design can alleviate the bottleneck of limited fronthaul while achieving reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters, and establish its convergence to stationary solutions. Finally, simulations verify the advantages of the proposed scheme over the state-of-the-art baseline schemes.

Connected through hyperedge HE-1 (induced by source paper "energy efficient processing and robust wireless cooperative transmission for edge inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "reliable federated learning for mobile networks"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "limited feedback in single and multi user mimo systems with finite bit adcs"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-2 (induced by source paper "computation offloading for iot in c ran optimization and deep learning"):
  with members:
    - "non stationarities in extra large scale massive mimo"
    - "cross layer design for mission critical iot in mobile edge computing systems"
    - "learning to optimize training deep neural networks for interference management"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "deep learning in mobile and wireless networking a survey"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"

Connected through hyperedge HE-3 (induced by source paper "mixed timescale beamforming and power splitting for massive mimo aided swipt iot network"):
  with members:
    - "fractional programming for communication systems part ii uplink scheduling via matching"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "practical non linear energy harvesting model and resource allocation for swipt systems"
    - "joint transmit beamforming and receive power splitting for miso swipt systems"
    - "mimo broadcasting for simultaneous wireless information and power transfer"

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)