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: "distributed vector estimation for power and bandwidth constrained wireless sensor networks"
  Abstract: The authors consider the distributed estimation of a Gaussian vector with a linear observation model in an inhomogeneous wireless sensor network, in which a fusion center (FC) reconstructs the unknown vector using a linear estimator. Sensors employ uniform multi-bit quantizers and binary PSK modulation, and they communicate with the FC over orthogonal power- and bandwidth-constrained wireless channels. The authors study transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the mean-square error (MSE). In particular, they derive two closed-form upper bounds on the MSE in terms of the optimization parameters and propose “coupled” and “decoupled” resource allocation schemes that minimize these bounds. The authors show that the bounds are good approximations of the simulated MSE and that the performance of the proposed schemes approaches the clairvoyant centralized estimation when the total transmit power and bandwidth is very large. They investigate how the power and rate allocations are dependent on the sensors’ observation qualities and channel gains and on the total transmit power and bandwidth constraints. Their simulations corroborate their analytical results and demonstrate the superior performance of the proposed algorithms.

Connected through hyperedge HE-1 (induced by source paper "trajectory design for distributed estimation in uav enabled wireless sensor network"):
  with members:
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "energy efficient data collection in uav enabled wireless sensor network"
    - "energy efficient uav communication with trajectory optimization"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "approximation algorithms for tsp with neighborhoods in the plane"

Connected through hyperedge HE-2 (induced by source paper "secure and private cloud storage systems with random linear fountain codes"):
  with members:
    - "private information retrieval from mds coded data in distributed storage systems"
    - "cloud based deep learning of big eeg data for epileptic seizure prediction"
    - "a secure approach for caching contents in wireless ad hoc networks"
    - "multihop caching aided coded multicasting for the next generation of cellular networks"
    - "optimizing pilot overhead for ultra reliable short packet transmission"
    - "secure repairable fountain codes"
    - "throughput analysis of decentralized coded content caching in cellular networks"
    - "capacity of cellular networks with femtocache"

Connected through hyperedge HE-3 (induced by source paper "on bayesian fisher information maximization for distributed vector estimation"):
  with members:
    - "on quantizer design for distributed bayesian estimation in sensor networks"
    - "distributed estimation of a parametric field algorithms and performance analysis"
    - "optimal identical binary quantizer design for distributed estimation"

Connected through hyperedge HE-4 (induced by source paper "power allocation and cooperative diversity in two way non regenerative cognitive radio networks"):
  with members:
    - "capacity of cellular networks with femtocache"
    - "optimal linear transceiver designs for cognitive two way relay networks"
    - "a simple cooperative diversity method based on network path selection"

Connected through hyperedge HE-5 (induced by source paper "synchronization in networks of identical systems via pinning application to distributed secondary control of microgrids"):
  with members:
    - "bounded stability in networked systems with parameter mismatch and adaptive decentralized estimation"

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)