human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "an improved approximation algorithm for the hard uniform capacitated k median problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved local search based approximation algorithm for hard uniform capacitated k median problem")
  Member "improved distributed principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the communication complexity of optimization")
  Member "decompositions of triangle dense graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mrattractor detecting communities from large scale graphs")
  Member "approximation algorithms for hard capacitated k facility location problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "facility location problem with capacity constraints algorithmic and mechanism design perspectives")
  Member "bi factor approximation algorithms for hard capacitated k median problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reconciliation k median clustering with non polarized representatives")
  Member "greedy facility location algorithms analyzed using dual fitting with factor revealing lp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cache optimization models and algorithms")

Question: Please tell me which class the source paper 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)