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: "analysis of sum weight like algorithms for averaging in wireless sensor networks"
  Abstract: Distributed estimation of the average value over a Wireless Sensor Network has recently received a lot of attention. Most papers consider single variable sensors and communications with feedback (e.g., peer-to-peer communications). However, in order to use efficiently the broadcast nature of the wireless channel, communications without feedback are advocated. To ensure the convergence in this feedback-free case, the recently-introduced Sum-Weight-like algorithms which rely on two variables at each sensor are a promising solution. In this paper, the convergence towards the consensus over the average of the initial values is analyzed in depth. Furthermore, it is shown that the squared error decreases exponentially with the time. In addition, a powerful algorithm relying on the Sum-Weight structure and taking into account the broadcast nature of the channel is proposed.

Connected through hyperedge HE-1 (induced by source paper "sample greedy gossip distributed kalman filter"):
  with members:
    - "greedy gossip with eavesdropping"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-2 (induced by source paper "distributed optimization using the primal dual method of multipliers"):
  with members:
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "online alternating direction method"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-3 (induced by source paper "finite time convergent gossiping"):
  with members:
    - "consensus over random graph processes network borel cantelli lemmas for almost sure convergence"
    - "finite time consensus using stochastic matrices with positive diagonals"
    - "the evolution of beliefs over signed social networks"
    - "opinion fluctuations and disagreement in social networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "order optimal consensus through randomized path averaging"
    - "fast distributed algorithms for computing separable functions"

Connected through hyperedge HE-4 (induced by source paper "push sum with transmission failures"):
  with members:
    - "average consensus on general strongly connected digraphs"
    - "gossip algorithms for distributed signal processing"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-5 (induced by source paper "on the learning behavior of adaptive networks part i transient analysis"):
  with members:
    - "dictionary learning over distributed models"
    - "distributed policy evaluation under multiple behavior strategies"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "diffusion adaptation over networks"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

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)