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: "unitary precoding and basis dependency of mmse performance for gaussian erasure channels"
  Abstract: We consider the transmission of a Gaussian vector source over a multi-dimensional Gaussian channel where a random or a fixed subset of the channel outputs are erased. Within the setup where the only encoding operation allowed is a linear unitary transformation on the source, we investigate the MMSE performance, both in average, and also in terms of guarantees that hold with high probability as a function of the system parameters. Under the performance criterion of average MMSE, necessary conditions that should be satisfied by the optimal unitary encoders are established and explicit solutions for a class of settings are presented. For random sampling of signals that have a low number of degrees of freedom, we present MMSE bounds that hold with high probability. Our results illustrate how the spread of the eigenvalue distribution and the unitary transformation contribute to these performance guarantees. The performance of the discrete Fourier transform (DFT) is also investigated. As a benchmark, we investigate the equidistant sampling of circularly wide-sense stationary (c.w.s.s.) signals, and present the explicit error expression that quantifies the effects of the sampling rate and the eigenvalue distribution of the covariance matrix of the signal. #R##N#These findings may be useful in understanding the geometric dependence of signal uncertainty in a stochastic process. In particular, unlike information theoretic measures such as entropy, we highlight the basis dependence of uncertainty in a signal with another perspective. The unitary encoding space restriction exhibits the most and least favorable signal bases for estimation.

Connected through hyperedge HE-1 (induced by source paper "lattice erasure codes of low rank with noise margins"):
  with members:
    - "analog coding of a source with erasures"
    - "graph based random access for the collision channel without feedback capacity bound"

Connected through hyperedge HE-2 (induced by source paper "performance bounds for remote estimation under energy harvesting constraints"):
  with members:
    - "linear transmission of composite gaussian measurements over a fading channel under delay constraints"
    - "source channel coding under energy delay and buffer constraints"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "simultaneous information and power transfer for broadband wireless systems"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "support recovery with sparsely sampled free random matrices"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-3 (induced by source paper "transmission strategies for remote estimation with an energy harvesting sensor"):
  with members:
    - "energy harvesting wireless communications a review of recent advances"
    - "source channel coding under energy delay and buffer constraints"
    - "dynamic compression transmission for energy harvesting multihop networks with correlated sources"
    - "near optimal energy control and approximate capacity of energy harvesting communication"
    - "support recovery with sparsely sampled free random matrices"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"
    - "broadcasting with an energy harvesting rechargeable transmitter"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

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)