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: "performance of linear field reconstruction techniques with noise and uncertain sensor locations"
  Abstract: We consider a wireless sensor network, sampling a bandlimited field, described by a limited number of harmonics. Sensor nodes are irregularly deployed over the area of interest or subject to random displacement; in addition sensors measurements are affected by noise. Our goal is to obtain a high quality reconstruction of the field, with the mean square error (MSE) of the estimate as performance metric. In particular, we analytically derive the performance of several reconstruction/estimation techniques based on linear filtering. For each technique, we obtain the MSE, as well as its asymptotic expression in the case where the number of field-harmonics and the number of sensors grow to infinity, while their ratio is kept constant. Through numerical simulations, we show the validity of the asymptotic analysis, even for a small number of sensors. We provide some novel guidelines for the design of sensor networks when many parameters, such as field bandwidth, number of sensors, reconstruction quality, and sensor displacement characteristics, to be traded off.

Connected through hyperedge HE-1 (induced by source paper "on bandlimited field estimation from samples recorded by a location unaware mobile sensor"):
  with members:
    - "on bandlimited signal reconstruction from the distribution of unknown sampling locations"
    - "sampling and reconstruction of spatial fields using mobile sensors"

Connected through hyperedge HE-2 (induced by source paper "on bandlimited signal reconstruction from the distribution of unknown sampling locations"):
  with members:
    - "sampling and reconstruction of spatial fields using mobile sensors"

Connected through hyperedge HE-3 (induced by source paper "convolution operations arising from vandermonde matrices"):
  with members:
    - "eigen inference for energy estimation of multiple sources"
    - "finite dimensional statistical inference"
    - "field reconstruction in sensor networks with coverage holes and packet losses"
    - "channel capacity estimation using free probability theory"
    - "reconstruction of multidimensional signals from irregular noisy samples"
    - "vandermonde frequency division multiplexing for cognitive radio"
    - "free deconvolution for signal processing applications"

Connected through hyperedge HE-4 (induced by source paper "field reconstruction in sensor networks with coverage holes and packet losses"):
  with members:
    - "convolution operations arising from vandermonde matrices"
    - "reconstruction of multidimensional signals from irregular noisy samples"

Connected through hyperedge HE-5 (induced by source paper "random vandermonde matrices part ii applications"):
  with members:
    - "cooperative spectrum sensing using random matrix theory"
    - "channel capacity estimation using free probability theory"
    - "random vandermonde matrices part i fundamental results"
    - "free deconvolution for signal processing applications"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "on the d dimensional quasi equally spaced sampling"):
  with members:
    - "reconstruction of multidimensional signals from irregular noisy samples"
    - "random vandermonde matrices part ii applications"
    - "random vandermonde matrices part i fundamental results"
    - "free deconvolution for signal processing applications"

Connected through hyperedge HE-7 (induced by source paper "asymptotic behaviour of random vandermonde matrices with entries on the unit circle"):
  with members:
    - "cooperative spectrum sensing using random matrix theory"
    - "channel capacity estimation using free probability theory"
    - "reconstruction of multidimensional signals from irregular noisy samples"

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.OH(Other Computer Science)