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: "a unique nonnegative solution to an underdetermined system from vectors to matrices"
  Abstract: This paper investigates the uniqueness of a nonnegative vector solution and the uniqueness of a positive semidefinite matrix solution to underdetermined linear systems. A vector solution is the unique solution to an underdetermined linear system only if the measurement matrix has a row-span intersecting the positive orthant. Focusing on two types of binary measurement matrices, Bernoulli 0-1 matrices and adjacency matrices of general expander graphs, we show that, in both cases, the support size of a unique nonnegative solution can grow linearly, namely O(n), with the problem dimension n . We also provide closed-form characterizations of the ratio of this support size to the signal dimension. For the matrix case, we show that under a necessary and sufficient condition for the linear compressed observations operator, there will be a unique positive semidefinite matrix solution to the compressed linear observations. We further show that a randomly generated Gaussian linear compressed observations operator will satisfy this condition with overwhelmingly high probability.

Connected through hyperedge HE-1 (induced by source paper "machine learning for geometrically consistent angular spread function estimation in massive mimo"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning methods for improved decoding of linear codes"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "deep learning for joint source channel coding of text"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-2 (induced by source paper "low rank positive semidefinite matrix recovery from corrupted rank one measurements"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "compressive principal component pursuit"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-3 (induced by source paper "a scalable and statistically robust beam alignment technique for mm wave systems"):
  with members:
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "hybrid beamforming for massive mimo a survey"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-4 (induced by source paper "perfect recovery conditions for non negative sparse modeling"):
  with members:
    - "sparse recovery of positive signals with minimal expansion"

Connected through hyperedge HE-5 (induced by source paper "active sensing of social networks"):
  with members:
    - "signal processing on graphs modeling causal relations in big data"
    - "the social system identification problem"
    - "analysis and control of beliefs in social networks"
    - "graph theoretic distributed inference in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "social structure of facebook networks"
    - "higher dimensional consensus learning in large scale networks"

Connected through hyperedge HE-6 (induced by source paper "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "low rank matrix recovery from rank one measurements"
    - "ripless compressed sensing from anisotropic measurements"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

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)