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: "gossip algorithms for distributed signal processing"
  Abstract: Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.

Connected through hyperedge HE-1 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"

Connected through hyperedge HE-2 (induced by source paper "decentralized deep learning with arbitrary communication compression"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "advances and open problems in federated learning"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "robust and communication efficient collaborative learning"
    - "an exact quantized decentralized gradient descent algorithm"
    - "texttt deepsqueeze decentralization meets error compensated compression"

Connected through hyperedge HE-3 (induced by source paper "distributed voting in beep model"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "beeping a maximal independent set fast"
    - "deterministic rendezvous with detection using beeps"
    - "counting in one hop beeping networks"
    - "naming a channel with beeps"
    - "asynchronous broadcasting with bivalent beeps"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "design patterns in beeping algorithms examples emulation and analysis"

Connected through hyperedge HE-4 (induced by source paper "clustering with distributed data"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed k means algorithm"
    - "distributed k means and k median clustering on general topologies"
    - "distributed strongly convex optimization"
    - "scalable k means"
    - "fast distributed gradient methods"

Connected through hyperedge HE-5 (induced by source paper "decentralized cooperative stochastic bandits"):
  with members:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "delay and cooperation in nonstochastic bandits"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "distributed exploration in multi armed bandits"
    - "online learning under delayed feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

Connected through hyperedge HE-6 (induced by source paper "cyber social systems modeling inference and optimal design"):
  with members:
    - "structural cost optimal design of sensor networks for distributed estimation"
    - "distributed estimation recovery under sensor failure"
    - "observational equivalence in system estimation contractions in complex networks"
    - "active sensing of social networks"
    - "measurement partitioning and observational equivalence in state estimation"
    - "graph theoretic distributed inference in social networks"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-7 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part ii performance analysis"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "network lasso clustering and optimization in large graphs"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "multitask diffusion adaptation over networks"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"

Connected through hyperedge HE-8 (induced by source paper "optimal hierarchical signaling for quadratic cost measures and general distributions a copositive program characterization"):
  with members:
    - "robust sensor design against multiple attackers with misaligned control objectives"
    - "deception as defense framework for cyber physical systems"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "hierarchical multistage gaussian signaling games in noncooperative communication and control systems"
    - "algorithmic bayesian persuasion"
    - "estimation with strategic sensors"

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)