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 "on communication complexity of classification problems"):
  Abstract: This work introduces a model of distributed learning in the spirit of Yao's communication complexity model. We consider a two-party setting, where each of the players gets a list of labelled examplesand they communicate in order to jointly perform some learning task. To naturally fit into the framework of learning theory, we allow the players to send each other labelled examples, where each example costs one unit of communication. This model can also be thought of as a distributed version of sample compression schemes. #R##N#We study several fundamental questions in this model. For example, we define the analogues of the complexity classes P, NP and coNP, and show that in this model P equals the intersection of NP and coNP. The proof does not seem to follow from the analogous statement in classical communication complexity; in particular, our proof uses different techniques, including boosting and metric properties of VC classes. #R##N#This framework allows to prove, in the context of distributed learning, unconditional separations between various learning contexts, like realizable versus agnostic learning, and proper versus improper learning. The proofs here are based on standard ideas from communication complexity as well as learning theory and geometric constructions in Euclidean space. As a corollary, we also obtain lower bounds that match the performance of algorithms from previous works on distributed classification.

Includes members:
  Member "a learning problem that is independent of the set theory zfc axioms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the bounds of function approximations")
  Member "communication complexity of distributed convex learning and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "sample compression schemes for vc classes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal collusion free teaching")
  Member "communication efficient distributed optimization using an approximate newton type method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "distributed learning communication complexity and privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on distributed machine learning")
  Member "optimal distributed online prediction using mini batches":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication optimization strategies for distributed deep learning a survey")

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)