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: "blind over the air computation and data fusion via provable wirtinger flow"
  Abstract: Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired functions of distributed sensing data by exploiting superposed data transmission in multiple access channels. To overcome its reliance on channel station information (CSI), this work proposes a novel blind over-the-air computation (BlairComp) without requiring CSI access, particularly for low complexity and low latency IoT networks. To solve the resulting non-convex optimization problem without the initialization dependency exhibited by the solutions of a number of recently proposed efficient algorithms, we develop a Wirtinger flow solution to the BlairComp problem based on random initialization. To analyze the resulting efficiency, we prove its statistical optimality and global convergence guarantee. Specifically, in the first stage of the algorithm, the iteration of randomly initialized Wirtinger flow given sufficient data samples can enter a local region that enjoys strong convexity and strong smoothness within a few iterations. We also prove the estimation error of BlairComp in the local region to be sufficiently small. We show that, at the second stage of the algorithm, its estimation error decays exponentially at a linear convergence rate.

Connected through hyperedge HE-1 (induced by source paper "learning one hidden layer neural networks via provable gradient descent with random initialization"):
  with members:
    - "nonconvex demixing from bilinear measurements"
    - "the global optimization geometry of shallow linear neural networks"
    - "nonconvex matrix factorization from rank one measurements"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "how to escape saddle points efficiently"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"

Connected through hyperedge HE-2 (induced by source paper "optimal power control for over the air computation in fading channels"):
  with members:
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
    - "over the air function computation in sensor networks"
    - "on ergodic sum capacity of fading cognitive multiple access and broadcast channels"
    - "optimal power allocation for fading channels in cognitive radio networks ergodic capacity and outage capacity"
    - "compute and forward harnessing interference through structured codes"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-3 (induced by source paper "over the air computation via intelligent reflecting surfaces"):
  with members:
    - "the roadmap to 6g ai empowered wireless networks"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "intelligent reflecting surface enhanced wireless network joint active and passive beamforming design"
    - "admm based fast algorithm for multi group multicast beamforming in large scale wireless systems"
    - "massive machine type communication with data aggregation and resource scheduling"

Connected through hyperedge HE-4 (induced by source paper "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"):
  with members:
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "wirelessly powered data aggregation for iot via over the air functional computation beamforming and power control"
    - "hybrid beamforming via the kronecker decomposition for the millimeter wave massive mimo systems"
    - "inference from randomized transmissions by many backscatter sensors"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "communication efficient learning of deep networks from decentralized data"
    - "over the air function computation in sensor networks"
    - "massive mimo for next generation wireless systems"

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)