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 "multiscale gossip for efficient decentralized averaging in wireless packet networks"):
  Abstract: This paper describes and analyzes a hierarchical gossip algorithm for solving the distributed average consensus problem in wireless sensor networks. The network is recursively partitioned into subnetworks. Initially, nodes at the finest scale gossip to compute local averages. Then, using geographic routing to enable gossip between nodes that are not directly connected, these local averages are progressively fused up the hierarchy until the global average is computed. We show that the proposed hierarchical scheme with $k$ levels of hierarchy is competitive with state-of-the-art randomized gossip algorithms, in terms of message complexity, achieving $\epsilon$-accuracy with high probability after $O\big(n \log \log n \log \frac{kn}{\epsilon} \big)$ messages. Key to our analysis is the way in which the network is recursively partitioned. We find that the optimal scaling law is achieved when subnetworks at scale $j$ contain $O(n^{(2/3)^j})$ nodes; then the message complexity at any individual scale is $O(n \log \frac{kn}{\epsilon})$, and the total number of scales in the hierarchy grows slowly, as $\Theta(\log \log n)$. Another important consequence of hierarchical construction is that the longest distance over which messages are exchanged is $O(n^{1/3})$ hops (at the highest scale), and most messages (at lower scales) travel shorter distances. In networks that use link-level acknowledgements, this results in less congestion and resource usage by reducing message retransmissions. Simulations illustrate that the proposed scheme is more message-efficient than existing state-of-the-art randomized gossip algorithms based on averaging along paths.

Includes members:
  Member "location aided fast distributed consensus in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convergence speed of the consensus algorithm with interference and sparse long range connectivity")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inverse graph learning over optimization networks")
  Member "optimization and analysis of distributed averaging with short node memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient distributed online prediction and stochastic optimization with approximate distributed averaging")
  Member "distributed averaging via lifted markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "consensus over random graph processes network borel cantelli lemmas for almost sure convergence")
  Member "hierarchical routing over dynamic wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparison based learning from weak oracles")
  Member "polynomial filtering for fast convergence in distributed consensus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "average consensus by graph filtering new approach explicit convergence rate and optimal design")
  Member "geographic gossip efficient averaging for sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantifying differential privacy of gossip protocols in general networks")
  Member "order optimal consensus through randomized path averaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")

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.DC(Distributed, Parallel, and Cluster Computing)