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: "data driven resource allocation for distributed learning"
  Abstract: In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points are often belonging to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex", we propose data dependent dispatching that takes advantage of such structures. Our main technical contribution is to provide algorithms with provable guarantees for data-dependent dispatching, that partition the data in a way that satisfies important conditions for accurate distributed learning, including fault tolerance and balancedness. We show the effectiveness of our method over the widely used random partitioning scheme in several real world image and advertising datasets.

Connected through hyperedge HE-1 (induced by source paper "faster balanced clusterings in high dimension"):
  with members:
    - "privacy preserving clustering with constraints"
    - "balanced k center clustering when k is a constant"
    - "capacitated center problems with two sided bounds and outliers"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "distributed balanced partitioning via linear embedding"
    - "faster algorithms for the constrained k means problem"

Connected through hyperedge HE-2 (induced by source paper "robust communication optimal distributed clustering algorithms"):
  with members:
    - "perturbation resilient clustering for k center and related problems via lp relaxations"
    - "distributed partial clustering"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "communication optimal distributed clustering"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "k center clustering under perturbation resilience"
    - "a bi criteria approximation algorithm for k means"

Connected through hyperedge HE-3 (induced by source paper "capacitated center problems with two sided bounds and outliers"):
  with members:
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "improved approximation algorithms for capacitated fault tolerant k center"
    - "constant factor approximation for capacitated k center with outliers"
    - "matroid and knapsack center problems"
    - "fast clustering with lower bounds no customer too far no shop too small"
    - "approximating k median via pseudo approximation"
    - "lp rounding for k centers with non uniform hard capacities"
    - "clustering with diversity"

Connected through hyperedge HE-4 (induced by source paper "balanced k center clustering when k is a constant"):
  with members:
    - "capacitated center problems with two sided bounds and outliers"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "distributed balanced partitioning via linear embedding"
    - "constant factor approximation for capacitated k center with outliers"
    - "fast clustering with lower bounds no customer too far no shop too small"
    - "lp rounding for k centers with non uniform hard capacities"

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)