human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "asynchronous code division random access using convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on throughput of compressive random access for one short message delivery in iot")
  Member "structured compressed sensing from theory to applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "serial quantization for sparse time sequences")
  Member "large system analysis of multiuser detection with an unknown number of users a high snr approach":
    also connected through other hyperedges:
      (none)
  Member "noise folding in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "xampling signal acquisition and processing in union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")
  Member "time delay estimation from low rate samples a union of subspaces approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image guided depth sampling and reconstruction")
  Member "support recovery of sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cooperative sparsity pattern recovery in distributed networks via distributed omp")

Question: Please tell me which class the source paper 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)