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 "block compressed sensing based distributed device detection for m2m communications"):
  Abstract: In this work, we utilize the framework of compressed sensing (CS) for distributed device detection and resource allocation in large-scale machine-to-machine (M2M) communication networks. The devices deployed in the network are partitioned into clusters according to some pre-defined criteria. Moreover, the devices in each cluster are assigned a unique signature of a particular design that can be used to indicate their active status to the network. The proposed scheme in this work mainly consists of two essential steps: (i) The base station (BS) detects the active clusters and the number of active devices in each cluster using a novel block sketching algorithm, and then assigns a certain amount of resources accordingly. (ii) Each active device detects its ranking among all the active devices in its cluster using an enhanced greedy algorithm and accesses the corresponding resource for transmission based on the ranking. By exploiting the correlation in the device behaviors and the sparsity in the activation pattern of the M2M devices, the device detection problem is thus tackled as a CS support recovery procedure for a particular binary block-sparse signal $x\in\mathbb{B}^N$ -- with block sparsity $K_B$ and in-block sparsity $K_I$ over block size $d$. Theoretical analysis shows that the activation pattern of the M2M devices can be reliably reconstructed within an acquisition time of $\mathcal{O}(\max\{K_B\log N, K_BK_I\log d\})$, which achieves a better scaling and less computational complexity of $\mathcal{O}(N(K_I^2+\log N))$ compared with standard CS algorithms. Moreover, extensive simulations confirm the robustness of the proposed scheme in the detection process, especially in terms of higher detection probability and reduced access delay when compared with conventional schemes like LTE random access (RA) procedure and classic cluster-based access approaches.

Includes members:
  Member "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements":
    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 "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a sharp recovery condition for sparse signals with partial support information via orthogonal matching pursuit")
  Member "newton like method with diagonal correction for distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed second order methods with variable number of working nodes")
  Member "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when does omp achieve support recovery with continuous dictionaries")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "condition numbers of gaussian random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)