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 "coded distributed diversity a novel distributed reception technique for wireless communication systems"):
  Abstract: In this paper, we consider a distributed reception scenario where a transmitter broadcasts a signal to multiple geographically separated receive nodes over fading channels, and each node forwards a few bits representing a processed version of the received signal to a fusion center. The fusion center then tries to decode the transmitted signal based on the forwarded information from the receive nodes and possible channel state information. We show that there is a strong connection between the problem of minimizing the symbol error probability at the fusion center in distributed reception and channel coding in coding theory. This connection allows us to design a unified framework for coded distributed diversity reception. We focus on linear block codes such as simplex codes or first-order Reed-Muller codes, which achieve the Griesmer bound with equality, to maximize the diversity gain. Due to our approach's simplicity, no complex offline optimization process is needed to design the coding structure at distributed receive nodes for the proposed coded receive diversity technique. The proposed technique can support a wide array of distributed reception scenarios, e.g., arbitrary   $M$   -ary symbol transmission at the transmitter and received signal processing with multiple bits at the receive nodes. Numerical studies show that coded receive diversity can achieve practical symbol error rates even with moderate signal-to-noise ratio and numbers of the receive nodes.

Includes members:
  Member "channel estimation techniques for quantized distributed reception in mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "channel estimation for spatially temporally correlated massive mimo systems with one bit adcs")
  Member "distributed reception with spatial multiplexing mimo systems for the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint channel and data estimation for large mimo systems with low precision adcs")

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)