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: "efficient protocols for distributed classification and optimization"
  Abstract: In distributed learning, the goal is to perform a learning task over data distributed across multiple nodes with minimal (expensive) communication. Prior work (Daume III et al., 2012) proposes a general model that bounds the communication required for learning classifiers while allowing for $\eps$ training error on linearly separable data adversarially distributed across nodes. #R##N#In this work, we develop key improvements and extensions to this basic model. Our first result is a two-party multiplicative-weight-update based protocol that uses $O(d^2 \log{1/\eps})$ words of communication to classify distributed data in arbitrary dimension $d$, $\eps$-optimally. This readily extends to classification over $k$ nodes with $O(kd^2 \log{1/\eps})$ words of communication. Our proposed protocol is simple to implement and is considerably more efficient than baselines compared, as demonstrated by our empirical results. #R##N#In addition, we illustrate general algorithm design paradigms for doing efficient learning over distributed data. We show how to solve fixed-dimensional and high dimensional linear programming efficiently in a distributed setting where constraints may be distributed across nodes. Since many learning problems can be viewed as convex optimization problems where constraints are generated by individual points, this models many typical distributed learning scenarios. Our techniques make use of a novel connection from multipass streaming, as well as adapting the multiplicative-weight-update framework more generally to a distributed setting. As a consequence, our methods extend to the wide range of problems solvable using these techniques.

Connected through hyperedge HE-1 (induced by source paper "distributed and streaming linear programming in low dimensions"):
  with members:
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "communication steps for parallel query processing"
    - "sorting searching and simulation in the mapreduce framework"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "lower bounds for predecessor searching in the cell probe model"

Connected through hyperedge HE-2 (induced by source paper "on communication cost of distributed statistical estimation and dimensionality"):
  with members:
    - "distributed learning communication complexity and privacy"
    - "information equals amortized communication"

Connected through hyperedge HE-3 (induced by source paper "para active learning"):
  with members:
    - "distributed learning communication complexity and privacy"
    - "a reliable effective terascale linear learning system"
    - "online importance weight aware updates"
    - "agnostic active learning without constraints"
    - "adaptive bound optimization for online convex optimization"
    - "optimal distributed online prediction using mini batches"
    - "importance weighted active learning"

Connected through hyperedge HE-4 (induced by source paper "when distributed computation is communication expensive"):
  with members:
    - "communication steps for parallel query processing"
    - "distributed learning communication complexity and privacy"
    - "sorting searching and simulation in the mapreduce framework"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "tight bounds for distributed functional monitoring"

Connected through hyperedge HE-5 (induced by source paper "distributed k means and k median clustering on general topologies"):
  with members:
    - "principal component analysis and higher correlations for distributed data"
    - "approximating k median via pseudo approximation"
    - "distributed learning communication complexity and privacy"
    - "scalable k means"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-6 (induced by source paper "distributed exploration in multi armed bandits"):
  with members:
    - "distributed non stochastic experts"
    - "distributed learning communication complexity and privacy"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "distributed non stochastic experts"):
  with members:
    - "distributed learning communication complexity and privacy"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-8 (induced by source paper "distributed learning communication complexity and privacy"):
  with members:
    - "robust interactive learning"
    - "a learning theory approach to non interactive database privacy"
    - "what can we learn privately"

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.LG(Machine Learning)