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: "estimation in phase shift and forward wireless sensor networks"
  Abstract: We consider a network of single-antenna sensors that observe an unknown deterministic parameter. Each sensor applies a phase shift to the observation and the sensors simultaneously transmit the result to a multi-antenna fusion center (FC). Based on its knowledge of the wireless channel to the sensors, the FC calculates values for the phase factors that minimize the variance of the parameter estimate, and feeds this information back to the sensors. The use of a phase-shift-only transmission scheme provides a simplified analog implementation at the sensor, and also leads to a simpler algorithm design and performance analysis. We propose two algorithms for this problem, a numerical solution based on a relaxed semidefinite programming problem, and a closed-form solution based on the analytic constant modulus algorithm. Both approaches are shown to provide performance close to the theoretical bound. We derive asymptotic performance analyses for cases involving large numbers of sensors or large numbers of FC antennas, and we also study the impact of phase errors at the sensor transmitters. Finally, we consider the sensor selection problem, in which only a subset of the sensors is chosen to send their observations to the FC.

Connected through hyperedge HE-1 (induced by source paper "over the air computation systems optimization analysis and scaling laws"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "wireless networked control systems with coding free data transmission for industrial iot"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "optimal power control for over the air computation in fading channels"
    - "exploiting the superposition property of wireless communication for max consensus problems in multi agent systems"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "inference from randomized transmissions by many backscatter sensors"
    - "over the air function computation in sensor networks"

Connected through hyperedge HE-2 (induced by source paper "wireless networked control systems with coding free data transmission for industrial iot"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "mimo over the air computation for high mobility multi modal sensing"
    - "wireless network design for control systems a survey"
    - "state secrecy codes for networked linear systems"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "sensor scheduling in variance based event triggered estimation with packet drops"

Connected through hyperedge HE-3 (induced by source paper "optimized transmission for consensus in wireless sensor networks"):
  with members:
    - "massive mimo for wireless sensing with a coherent multiple access channel"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "designing unimodular codes via quadratic optimization"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-4 (induced by source paper "massive mimo for wireless sensing with a coherent multiple access channel"):
  with members:
    - "detection in analog sensor networks with a large scale antenna fusion center"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "detection performance in balanced binary relay trees with node and link failures"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-5 (induced by source paper "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"):
  with members:
    - "parameter tracking via optimal distributed beamforming in an analog sensor network"
    - "on scaling laws of diversity schemes in decentralized estimation"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-6 (induced by source paper "detection in analog sensor networks with a large scale antenna fusion center"):
  with members:
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "estimation diversity and energy efficiency in distributed sensing"

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)