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: "reduced dimension multiuser detection"
  Abstract: We present a reduced-dimension multiuser detector (RD-MUD) structure for synchronous systems that significantly decreases the number of required correlation branches at the receiver front-end, while still achieving performance similar to that of the conventional matched-filter (MF) bank. RD-MUD exploits the fact that, in some wireless systems, the number of active users may be small relative to the total number of users in the system. Hence, the ideas of analog compressed sensing may be used to reduce the number of correlators. The correlating signals used by each correlator are chosen as an appropriate linear combination of the users' spreading waveforms. We derive the probability-of-symbol-error when using two methods for recovery of active users and their transmitted symbols: the reduced-dimension decorrelating (RDD) detector, which combines subspace projection and thresholding to determine active users and sign detection for data recovery, and the reduced-dimension decision-feedback (RDDF) detector, which combines decision-feedback matching pursuit for active user detection and sign detection for data recovery. We derive probability of error bounds for both detectors, and show that the number of correlators needed to achieve a small probability-of-symbol-error is on the order of the logarithm of the number of users in the system. The theoretical performance results are validated via numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "an ialm ica based anti jamming ds cdma receiver for lms systems"):
  with members:
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "many access for small packets based on precoding and sparsity aware recovery"):
  with members:
    - "capacity of gaussian many access channels"
    - "a novel uplink data transmission scheme for small packets in massive mimo system"
    - "many access channels the gaussian case with random user activities"
    - "scma codebook design"
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-3 (induced by source paper "multiuser detection by map estimation with sum of absolute values relaxation"):
  with members:
    - "discrete signal reconstruction by sum of absolute values"

Connected through hyperedge HE-4 (induced by source paper "info greedy sequential adaptive compressed sensing"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "information theoretic bounds for adaptive sparse recovery"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "distance based and continuum fano inequalities with applications to statistical estimation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "performance guarantees for adaptive estimation of sparse signals"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "compressive sensing for spread spectrum receivers"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "gps signal acquisition via compressive multichannel sampling"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "noise folding in compressed sensing"
    - "demodulating subsampled direct sequence spread spectrum signals using compressive signal processing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "compressed sensing how sharp is the restricted isometry property"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "compressive link acquisition in multiuser communications"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "gps signal acquisition via compressive multichannel sampling"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "dynamic updating for ell_ 1 minimization"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "on off random access channels a compressed sensing framework"

Connected through hyperedge HE-7 (induced by source paper "demodulation of sparse ppm signals with low samples using trained rip matrix"):
  with members:
    - "sensing matrix optimization for block sparse decoding"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "reduced dimension multiuser detection detectors and performance guarantees"):
  with members:
    - "iterative hard thresholding for compressed sensing"

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)