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 "privacy for free communication efficient learning with differential privacy using sketches"):
  Abstract: Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection exists between methods for communication reduction and privacy preservation in the context of distributed machine learning. In particular, we prove that Count Sketch, a simple method for data stream summarization, has inherent differential privacy properties. Using these derived privacy guarantees, we propose a novel sketch-based framework (DiffSketch) for distributed learning, where we compress the transmitted messages via sketches to simultaneously achieve communication efficiency and provable privacy benefits. Our evaluation demonstrates that DiffSketch can provide strong differential privacy guarantees (e.g., $\varepsilon$= 1) and reduce communication by 20-50x with only marginal decreases in accuracy. Compared to baselines that treat privacy and communication separately, DiffSketch improves absolute test accuracy by 5%-50% while offering the same privacy guarantees and communication compression.

Includes members:
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "total stochastic gradient algorithms and applications in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "credit assignment techniques in stochastic computation graphs")
  Member "communication efficient distributed sgd with sketching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trends and advancements in deep neural network communication")
  Member "secure computation for machine learning with spdz":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "harmonic coding an optimal linear code for privacy preserving gradient type computation")
  Member "federated learning challenges methods and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "scalable and differentially private distributed aggregation in the shuffled model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "federated heavy hitters discovery with differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fedpaq a communication efficient federated learning method with periodic averaging and quantization")
  Member "enhancing the privacy of federated learning with sketching":
    also connected through other hyperedges:
      (none)

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)