human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "optimization and analysis of distributed averaging with short node memory"
  Abstract: Distributed averaging describes a class of network algorithms for the decentralized computation of aggregate statistics. Initially, each node has a scalar data value, and the goal is to compute the average of these values at every node (the so-called average consensus problem). Nodes iteratively exchange information with their neighbors and perform local updates until the value at every node converges to the initial network average. Much previous work has focused on algorithms where each node maintains and updates a single value; every time an update is performed, the previous value is forgotten. Convergence to the average consensus is achieved asymptotically. The convergence rate is fundamentally limited by network connectivity, and it can be prohibitively slow on topologies such as grids and random geometric graphs, even if the update rules are optimized. In this paper, we provide the first theoretical demonstration that adding a local prediction component to the update rule can significantly improve the convergence rate of distributed averaging algorithms. We focus on the case where the local predictor is a linear combination of the node's current and previous values (i.e., two memory taps), and our update rule computes a combination of the predictor and the usual weighted linear combination of values received from neighboring nodes. We derive the optimal mixing parameter for combining the predictor with the neighbors' values, and conduct a theoretical analysis of the improvement in convergence rate that can be achieved using this acceleration methodology. For a chain topology on N nodes, this leads to a factor of N improvement over standard consensus, and for a two-dimensional grid, our approach achieves a factor of ?N improvement.

Connected through hyperedge HE-1 (induced by source paper "efficient distributed online prediction and stochastic optimization with approximate distributed averaging"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "communication efficient distributed dual coordinate ascent"
    - "communication computation tradeoffs in consensus based distributed optimization"
    - "fast distributed gradient methods"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "optimal distributed online prediction using mini batches"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "distributed asynchronous algorithms for solving positive definite linear equations over dynamic networks"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"
    - "zero gradient sum algorithms for distributed convex optimization the continuous time case"

Connected through hyperedge HE-3 (induced by source paper "a distributed algorithm for solving positive definite linear equations over networks with membership dynamics"):
  with members:
    - "zero gradient sum algorithms for distributed convex optimization the continuous time case"

Connected through hyperedge HE-4 (induced by source paper "chebyshev polynomials in distributed consensus applications"):
  with members:
    - "polynomial filtering for fast convergence in distributed consensus"

Connected through hyperedge HE-5 (induced by source paper "gossip algorithms for distributed signal processing"):
  with members:
    - "location aided fast distributed consensus in wireless networks"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributed lossy averaging"
    - "polynomial filtering for fast convergence in distributed consensus"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "the impact of mobility on gossip algorithms"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "distributed and recursive parameter estimation in parametrized linear state space models"

Connected through hyperedge HE-6 (induced by source paper "multiscale gossip for efficient decentralized averaging in wireless packet networks"):
  with members:
    - "location aided fast distributed consensus in wireless networks"
    - "gossip algorithms for distributed signal processing"
    - "distributed averaging via lifted markov chains"
    - "hierarchical routing over dynamic wireless networks"
    - "polynomial filtering for fast convergence in distributed consensus"
    - "geographic gossip efficient averaging for sensor networks"
    - "order optimal consensus through randomized path averaging"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Question: Please tell me which class the center node 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)