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 "improved scaling law for activity detection in massive mimo systems"):
  Abstract: In this paper, we study the problem of activity detection (AD) in a massive MIMO setup, where the Base Station (BS) has $M \gg 1$ antennas. We consider a flat fading channel model where the $M$-dim channel vector of each user remains almost constant over a coherence block (CB) containing $D_c$ signal dimensions. We study a setting in which the number of potential users $K_c$ assigned to a specific CB is much larger than the dimension of the CB $D_c$ ($K_c \gg D_c$) but at each time slot only $A_c \ll K_c$ of them are active. Most of the previous results, based on compressed sensing, require that $A_c\le D_c$, which is a bottleneck in massive deployment scenarios such as Internet-of-Things (IoT) and Device-to-Device (D2D) communication. In this paper, we propose a novel scheme for AD and show that it overcomes this limitation when the number of antennas $M$ is sufficiently large. We also derive a scaling law on the parameters $(M, D_c, K_c, A_c)$ and also Signal-to-Noise Ratio (SNR) under which our proposed AD scheme succeeds. Our analysis indicates that with a CB of dimension $D_c$, and a sufficient number of BS antennas $M=O(A_c)$, one can identify the activity of $A_c=O(D_c^2/\log (\frac{K_c}{A_c}))$ active users, which is much larger than the previous bound $A_c=O(D_c)$ obtained via traditional compressed sensing techniques. In particular, in our proposed scheme one needs to pay only a negligible logarithmic penalty $O(\log (\frac{K_c}{A_c}))$ for increasing the number of potential users $K_c$, which makes it perfect for AD in IoT setups. We propose very low-complexity algorithms for AD and provide numerical simulations to illustrate the validity of our results.

Includes members:
  Member "on the fly large scale channel gain estimation for massive antenna array base stations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "sparse activity detection for massive connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "massive connectivity with massive mimo part i device activity detection and channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "a scalable and statistically robust beam alignment technique for mm wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a robust time domain beam alignment scheme for multi user wideband mmwave systems")
  Member "user activity detection in massive random access compressed sensing vs coded slotted aloha":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo")
  Member "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "low complexity massive mimo subspace estimation and tracking from low dimensional projections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an efficient beam and channel acquisition via sparsity map and joint angle delay power profile estimation for wideband massive mimo systems")
  Member "on the gap between rip properties and sparse recovery conditions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")

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)