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 "fixed symbol aided random access scheme for machine to machine communications"):
  Abstract: The massiveness of devices in crowded Machine-to-Machine (M2M) communications brings new challenges to existing random-access (RA) schemes, such as heavy signaling overhead and severe access collisions. In order to reduce the signaling overhead, we propose a fixed-symbol aided RA scheme where active devices access the network in a grant-free method, i.e., data packets are directly transmitted in randomly chosen slots. To further address the access collision which impedes the activity detection, one fixed symbol is inserted into each transmitted data packet in the proposed scheme. An iterative message passing based activity detection (MP-AD) algorithm is performed upon the received signal of this fixed symbol to detect the device activity in each slot. In addition, the deep neural network-aided MP-AD (DNN-MP-AD) algorithm is further designed to alleviate the correlation problem of the iterative message passing process. In the DNN-MP-AD algorithm, the iterative message passing process is transferred from a factor graph to a DNN. Weights are imposed on the messages in the DNN and further trained to improve the accuracy of the device activity detection. Finally, numerical simulations are provided for the throughput of the proposed RA scheme, the accuracy of the proposed MP-AD algorithm, as well as the improvement brought by the DNN-MP-AD algorithm.

Includes members:
  Member "deep learning methods for improved decoding of linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "sparse message passing based preamble estimation for crowded m2m communications":
    also connected through other hyperedges:
      (none)
  Member "massive connectivity with massive mimo part ii achievable rate characterization":
    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 "irregular repetition slotted aloha over the rayleigh block fading channel with capture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random noma with cross slot successive interference cancellation packet recovery")
  Member "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior")
  Member "iterative channel estimation using lse and sparse message passing for mmwave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems")
  Member "gaussian message passing iterative detection for mimo noma systems with massive access":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior")

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)