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: "signal amplitude estimation and detection from unlabeled binary quantized samples"
  Abstract: Signal amplitude estimation and detection from unlabeled quantized binary samples are studied, assuming that the order of the time indexes is completely unknown. First, maximum likelihood (ML) estimators are utilized to estimate both the permutation matrix and unknown signal amplitude under arbitrary but known signal shape and quantizer thresholds. Sufficient conditions are provided, under which an ML estimator can be found in polynomial time, and an alternating maximization algorithm is proposed to solve the general problem via good initialization. In addition, the statistical identifiability of the model is studied. Furthermore, an approximation of the generalized likelihood ratio test detector is adopted to detect the presence of the signal. In addition, an accurate approximation of the probability of successful permutation matrix recovery is derived, and explicit expressions are provided to reveal the relationship between the signal length and the number of quantizers. Finally, numerical simulations are performed to verify the theoretical results.

Connected through hyperedge HE-1 (induced by source paper "unlabelled sensing a sparse bayesian learning approach"):
  with members:
    - "signal recovery from unlabeled samples"
    - "unlabeled sensing with random linear measurements"
    - "on the computational intractability of exact and approximate dictionary learning"

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)