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 "distributed sensing with orthogonal multiple access to code or not to code"):
  Abstract: We consider the estimation distortion of a distributed sensing system with finite number of sensor nodes, in which the nodes observe a common phenomenon and transmit their observations to a fusion center over orthogonal channels. In particular, we investigate whether the coded scheme (separate source-channel coding) outperforms the uncoded scheme (joint source-channel coding) or not. To this end, we explicitly derive the estimation distortion of a coded heterogeneous sensing system with diverse node and channel configurations. Based on this result, we show that in a homogeneous sensing system with identical node and channel configurations, the coded scheme outperforms the uncoded scheme if the number of nodes is $K=1$ or $K=2$. For homogenous sensing systems with $K\geq3$ nodes and general heterogeneous sensing systems, we also present explicit conditions for the coded scheme to perform better than the uncoded scheme. Furthermore, we propose to minimize the estimation distortion of heterogeneous sensing systems with hybrid coding, i.e., some nodes use the coded scheme and other nodes use the uncoded scheme. To determine the optimal hybrid coding policy, we develop three greedy algorithms, in which the pure greedy algorithm minimizes distortion greedily, the group greedy algorithm improves performance by using a group of potential sub-polices, and the sorted greedy algorithm reduces computational complexity by using a pre-solved iteration order. Our numerical and Monte Carlo results show that the proposed algorithms closely approach the optimal policy in terms average estimation distortion.

Includes members:
  Member "wireless power transfer for distributed estimation in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimization vs reinforcement learning for wirelessly powered sensor networks")
  Member "a survey on non orthogonal multiple access for 5g networks research challenges and future trends":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective capacity of noma with finite blocklength for low latency communications")
  Member "optimized sensor collaboration for estimation of temporally correlated parameters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal power control for transmitting correlated sources with energy harvesting constraints")
  Member "delay limited source and channel coding of quasi stationary sources over block fading channels design and scaling laws":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy harvesting powered sensing in iot timeliness versus distortion")
  Member "estimation diversity and energy efficiency in distributed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reconstruction of correlated sources with energy harvesting constraints in delay constrained and delay tolerant communication scenarios")

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)