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: "asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions"
  Abstract: In this paper, we propose a sparse signal estimation algorithm that is suitable for many wireless communication systems, especially for the future millimeter wave and underwater communication systems. This algorithm is not only asymptotically optimal, but also robust to the distribution of nonzero entries of the sparse signal. Then, we derive its upper bound and lower bound, and show that the mean square error of the proposed algorithm can approach the minimum mean square error bound when the signal noise ratio goes to infinite or zero. Numerical simulations verify our theoretical analysis and also show that the proposed algorithm converges faster than existing algorithms, e.g., turbo-type signal recovery-discrete fourier transform, approximate message passing, etc.

Connected through hyperedge HE-1 (induced by source paper "reconfigurable intelligent surface for miso systems with proportional rate constraints"):
  with members:
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "large intelligent surface assisted wireless communication exploiting statistical csi"
    - "intelligent reflecting surface enhanced wireless network joint active and passive beamforming design"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-2 (induced by source paper "deep learning for ul dl channel calibration in generic massive mimo systems"):
  with members:
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "a framework for over the air reciprocity calibration for tdd massive mimo systems"
    - "neural offset min sum decoding"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "dnn aided block sparse bayesian learning for user activity detection and channel estimation in grant free non orthogonal random access"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "fixed symbol aided random access scheme for machine to machine communications"
    - "deep learning methods for improved decoding of linear codes"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"

Connected through hyperedge HE-4 (induced by source paper "dnn aided message passing based block sparse bayesian learning for joint user activity detection and channel estimation"):
  with members:
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"

Connected through hyperedge HE-5 (induced by source paper "reconfigurable intelligent surfaces for energy efficiency in wireless communication"):
  with members:
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "using any surface to realize a new paradigm for wireless communications"
    - "energy efficiency of mmwave massive mimo precoding with low resolution dacs"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "capacity degradation with modeling hardware impairment in large intelligent surface"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"

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)