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: "sensor networks with random links topology design for distributed consensus"
  Abstract: In a sensor network, in practice, the communication among sensors is subject to: 1) errors that can cause failures of links among sensors at random times; 2) costs; and 3) constraints, such as power, data rate, or communication, since sensors and networks operate under scarce resources. The paper studies the problem of designing the topology, i.e., assigning the probabilities of reliable communication among sensors (or of link failures) to maximize the rate of convergence of average consensus, when the link communication costs are taken into account, and there is an overall communication budget constraint. We model the network as a Bernoulli random topology and establish necessary and sufficient conditions for mean square sense (mss) and almost sure (a.s.) convergence of average consensus when network links fail. In particular, a necessary and sufficient condition is for the algebraic connectivity of the mean graph topology to be strictly positive. With these results, we show that the topology design with random link failures, link communication costs, and a communication cost constraint is a constrained convex optimization problem that can be efficiently solved for large networks by semidefinite programming techniques. Simulations demonstrate that the optimal design improves significantly the convergence speed of the consensus algorithm and can achieve the performance of a non-random network at a fraction of the communication cost.

Connected through hyperedge HE-1 (induced by source paper "matcha speeding up decentralized sgd via matching decomposition sampling"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "communication compression for decentralized training"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "stochastic gradient push for distributed deep learning"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "gradient sparsification for communication efficient distributed optimization"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "distributed topology design for network coding deployed large scale sensor networks"):
  with members:
    - "network coding based evolutionary network formation for dynamic wireless networks"
    - "cost efficient qos aware data acquisition point placement for advanced metering infrastructure"
    - "decoding delay minimization in inter session network coding"
    - "distributed rate allocation in inter session network coding"
    - "dynamic network formation with incomplete information"
    - "mac centered cooperation synergistic design of network coding multi packet reception and improved fairness to increase network throughput"
    - "relay selection with network coding in two way relay channels"

Connected through hyperedge HE-3 (induced by source paper "distributed averaging with random network graphs and noises"):
  with members:
    - "multi agent consensus with relative state dependent measurement noises"
    - "network based consensus averaging with general noisy channels"

Connected through hyperedge HE-4 (induced by source paper "delta operator based consensus analysis of multi agent networks with link failures"):
  with members:
    - "consensus of hybrid multi agent systems"

Connected through hyperedge HE-5 (induced by source paper "distributed gradient methods with variable number of working nodes"):
  with members:
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "hybrid deterministic stochastic methods for data fitting"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"

Connected through hyperedge HE-6 (induced by source paper "distributed inference for relay assisted sensor networks with intermittent measurements over fading channels"):
  with members:
    - "toward resource optimal consensus over the wireless medium"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "the role of persistent graphs in the agreement seeking of social networks"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "distributed decision through self synchronizing sensor networks in the presence of propagation delays and nonreciprocal channels"

Connected through hyperedge HE-7 (induced by source paper "sampled data consensus over random networks"):
  with members:
    - "opinion fluctuations and disagreement in social networks"
    - "how agreement and disagreement evolve over random dynamic networks"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-8 (induced by source paper "elm based distributed cooperative learning over networks"):
  with members:
    - "a distributed approach toward discriminative distance metric learning"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "big learning with bayesian methods"
    - "zero gradient sum algorithms for distributed convex optimization the continuous time case"
    - "graphlab a new framework for parallel machine learning"

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.IT(Information Theory)