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: "enabling fdd massive mimo through deep learning based channel prediction"
  Abstract: A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We completely remove this overhead by a deep-learning based channel extrapolation (or "prediction") approach and demonstrate that a neural network (NN) at the BS can infer the DL CSI centered around a frequency $f_\text{DL}$ by solely observing uplink (UL) CSI on a different, yet adjacent frequency band around $f_\text{UL}$; no more pilot/reporting overhead is needed than with a genuine time division duplex (TDD)-based system. The rationale is that scatterers and the large-scale propagation environment are sufficiently similar to allow a NN to learn about the physical connections and constraints between two neighboring frequency bands, and thus provide a well-operating system even when classic extrapolation methods, like the Wiener filter (used as a baseline for comparison throughout) fails. We study its performance for various state-of-the-art Massive MIMO channel models, and, even more so, evaluate the scheme using actual Massive MIMO channel measurements, rendering it to be practically feasible at negligible loss in spectral efficiency when compared to a genuine TDD-based system.

Connected through hyperedge HE-1 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "non stationarities in extra large scale massive mimo"
    - "multi band covariance interpolation with applications in massive mimo"
    - "yolov3 an incremental improvement"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-2 (induced by source paper "machine learning aided design of thinned antenna arrays for optimized network level performance"):
  with members:
    - "enabling simulation based optimization through machine learning a case study on antenna design"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "learning to optimize training deep neural networks for interference management"
    - "study of realistic antenna patterns in 5g mmwave cellular scenarios"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-3 (induced by source paper "deep learning for csi feedback based on superimposed coding"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "learning to detect"
    - "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"
    - "deep learning in physical layer communications"
    - "mimo channel information feedback using deep recurrent network"
    - "multi task learning as multi objective optimization"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "deep learning based csi feedback approach for time varying massive mimo channels"

Connected through hyperedge HE-4 (induced by source paper "enabling simulation based optimization through machine learning a case study on antenna design"):
  with members:
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "learning to optimize training deep neural networks for interference management"
    - "study of realistic antenna patterns in 5g mmwave cellular scenarios"
    - "a very brief introduction to machine learning with applications to communication systems"
    - "an introduction to deep learning for the physical layer"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-5 (induced by source paper "predicting device to device channels from cellular channel measurements a learning approach"):
  with members:
    - "millimeter wave beam selection using out of band spatial information"
    - "mode selection resource allocation and power control for d2d enabled two tier cellular network"

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)