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: "adaptive sensing using deterministic partial hadamard matrices"
  Abstract: This paper investigates the construction of deterministic matrices preserving the entropy of random vectors with a given probability distribution. In particular, it is shown that for random vectors having i.i.d. discrete components, this is achieved by selecting a subset of rows of a Hadamard matrix such that (i) the selection is deterministic (ii) the fraction of selected rows is vanishing. In contrast, it is shown that for random vectors with i.i.d. continuous components, no partial Hadamard matrix of reduced dimension allows to preserve the entropy. These results are in agreement with the results of Wu-Verdu on almost lossless analog compression. This paper is however motivated by the complexity attribute of Hadamard matrices, which allows the use of efficient and stable reconstruction algorithms. The proof technique is based on a polar code martingale argument and on a new entropy power inequality for integer-valued random variables.

Connected through hyperedge HE-1 (induced by source paper "a structured construction of optimal measurement matrix for noiseless compressed sensing via analog polarization"):
  with members:
    - "polarization of the r enyi information dimension for single and multi terminal analog compression"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "model based compressive sensing"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "on the rate of channel polarization"

Connected through hyperedge HE-2 (induced by source paper "multi terminal probabilistic compressed sensing"):
  with members:
    - "polarization of the r enyi information dimension for single and multi terminal analog compression"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-3 (induced by source paper "polarization of the renyi information dimension with applications to compressed sensing"):
  with members:
    - "lossless linear analog compression"
    - "degrees of freedom in vector interference channels"
    - "high girth matrices and polarization"
    - "almost lossless analog signal separation and probabilistic uncertainty relations"
    - "a new entropy power inequality for integer valued random variables"
    - "polar codes for the two user multiple access channel"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "achieving the secrecy capacity of wiretap channels using polar codes"

Connected through hyperedge HE-4 (induced by source paper "polarization of the r enyi information dimension for single and multi terminal analog compression"):
  with members:
    - "degrees of freedom in vector interference channels"
    - "a structured construction of optimal measurement matrix for noiseless compressed sensing via analog polarization"
    - "a new entropy power inequality for integer valued random variables"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "the effect of spatial coupling on compressive sensing"
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "a new entropy power inequality for integer valued random variables"):
  with members:
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"
    - "the entropy power inequality and mrs gerber s lemma for abelian groups of order 2 n"
    - "information theoretic proofs of entropy power inequalities"
    - "entropy power inequality for a family of discrete random variables"
    - "monotonicity thinning and discrete versions of the entropy power inequality"
    - "generalized entropy power inequalities and monotonicity properties of information"
    - "an extremal inequality motivated by multiterminal information theoretic problems"

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)