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: "on block coherence of frames"
  Abstract: Block coherence of matrices plays an important role in analyzing the performance of block compressed sensing recovery algorithms (Bajwa and Mixon, 2012). In this paper, we characterize two block coherence metrics: worst-case and average block coherence. First, we present lower bounds on worst-case block coherence, in both the general case and also when the matrix is constrained to be a union of orthobases. We then present deterministic matrix constructions based upon Kronecker products which obtain these lower bounds. We also characterize the worst-case block coherence of random subspaces. Finally, we present a flipping algorithm that can improve the average block coherence of a matrix, while maintaining the worst-case block coherence of the original matrix. We provide numerical examples which demonstrate that our proposed deterministic matrix construction performs well in block compressed sensing.

Connected through hyperedge HE-1 (induced by source paper "a survey of noma current status and open research challenges"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "noma an information theoretic perspective"
    - "fast harq over finite blocklength codes a technique for low latency reliable communication"
    - "on the performance of noma with hybrid arq"
    - "cooperative harq assisted noma scheme in large scale d2d networks"
    - "optimal joint power and subcarrier allocation for full duplex multicarrier non orthogonal multiple access systems"
    - "dynamic user clustering and power allocation for uplink and downlink non orthogonal multiple access noma systems"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"

Connected through hyperedge HE-2 (induced by source paper "improved support recovery guarantees for the group lasso with applications to structural health monitoring"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the degrees of freedom of the group lasso for a general design"
    - "subspace methods for joint sparse recovery"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "recovery of block sparse representations from noisy observations via orthogonal matching pursuit"
    - "structured compressed sensing from theory to applications"
    - "sparse recovery from combined fusion frame measurements"

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"
    - "from random matrix theory to coding theory volume of a metric ball in unitary group"
    - "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 "deterministic construction of rip matrices in compressed sensing from constant weight codes"):
  with members:
    - "a new construction for constant weight codes"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "robust principal component analysis"
    - "constructions for q ary constant weight codes"

Connected through hyperedge HE-5 (induced by source paper "explicit rip matrices in compressed sensing from algebraic geometry"):
  with members:
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "robust principal component analysis"

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)