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: "from random matrix theory to coding theory volume of a metric ball in unitary group"
  Abstract: Volume estimates of metric balls in manifolds find diverse applications in information and coding theory. In this paper, some new results for the volume of a metric ball in unitary group are derived via various tools from random matrix theory. The first result is an integral representation of the exact volume, which involves a Toeplitz determinant of Bessel functions. The connection to matrix-variate hypergeometric functions and Szeg\H{o}'s strong limit theorem lead independently from the finite size formula to an asymptotic one. The convergence of the limiting formula is exceptionally fast due to an underlying mock-Gaussian behavior. The proposed volume estimate enables simple but accurate analytical evaluation of coding-theoretic bounds of unitary codes. In particular, the Gilbert-Varshamov lower bound and the Hamming upper bound on cardinality as well as the resulting bounds on code rate and minimum distance are derived. Moreover, bounds on the scaling law of code rate are found. Lastly, a closed-form bound on diversity sum relevant to unitary space-time codes is obtained, which was only computed numerically in literature.

Connected through hyperedge HE-1 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "robust estimates of covariance matrices in the large dimensional regime"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "on the theorem of uniform recovery of random sampling matrices"

Connected through hyperedge HE-2 (induced by source paper "on the similarity between nakagami m fading distribution and the gaussian ensembles of random matrix theory"):
  with members:
    - "a new framework for the performance analysis of wireless communications under hoyt nakagami q fading"

Connected through hyperedge HE-3 (induced by source paper "density of spherically embedded stiefel and grassmann codes"):
  with members:
    - "volume of metric balls in high dimensional complex grassmann manifolds"
    - "on block coherence of frames"
    - "constructions of grassmannian simplices"
    - "linear programming bounds for unitary space time codes"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"

Connected through hyperedge HE-4 (induced by source paper "volume of metric balls in high dimensional complex grassmann manifolds"):
  with members:
    - "normalized volume of hyperball in complex grassmann manifold and its application in large scale mu mimo communication systems"
    - "multiuser diversity for secrecy communications using opportunistic jammer selection secure dof and jammer scaling law"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"
    - "a feedback reduction technique for mimo broadcast channels"

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)