human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "discrimination on the grassmann manifold fundamental limits of subspace classifiers"):
  Abstract: We present fundamental limits on the reliable classification of linear and affine subspaces from noisy, linear features. Drawing an analogy between discrimination among subspaces and communication over vector wireless channels, we propose two Shannon-inspired measures to characterize asymptotic classifier performance. First, we define the classification capacity, which characterizes necessary and sufficient conditions for the misclassification probability to vanish as the signal dimension, the number of features, and the number of subspaces to be discerned all approach infinity. Second, we define the diversity-discrimination tradeoff which, by analogy with the diversity-multiplexing tradeoff of fading vector channels, characterizes relationships between the number of discernible subspaces and the misclassification probability as the noise power approaches zero. We derive upper and lower bounds on these measures which are tight in many regimes. Numerical results, including a face recognition application, validate the results in practice.

Includes members:
  Member "compressive classification and the rare eclipse problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets")
  Member "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive classification and the rare eclipse problem")
  Member "sparse signal processing with linear and non linear observations a unified shannon theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models")
  Member "learning robust subspace clustering":
    also connected through other hyperedges:
      (none)
  Member "support recovery with sparsely sampled free random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "communications inspired linear discriminant analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information")
  Member "on the capacity of large mimo block fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel")

Question: Please tell me which class the source paper 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)