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: "towards geo distributed machine learning"
  Abstract: Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is "born" geographically distributed. On the other hand, many machine learning applications require a global view of such data in order to achieve the best results. These types of applications form a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML). Such applications need to cope with: 1) scarce and expensive cross-data center bandwidth, and 2) growing privacy concerns that are pushing for stricter data sovereignty regulations. Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally. As machine learning algorithms are communication-intensive, the cost of centralizing the data is thought to be offset by the lower cost of intra-data center communication during training. In this work, we show that the current centralized practice can be far from optimal, and propose a system for doing geo-distributed training. Furthermore, we argue that the geo-distributed approach is structurally more amenable to dealing with regulatory constraints, as raw data never leaves the source data center. Our empirical evaluation on three real datasets confirms the general validity of our approach, and shows that GDML is not only possible but also advisable in many scenarios.

Connected through hyperedge HE-1 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "a survey on geographically distributed big data processing using mapreduce"):
  with members:
    - "security and privacy aspects in mapreduce on clouds a survey"
    - "the bigdawg polystore system and architecture"
    - "tensorflow a system for large scale machine learning"
    - "medusa an efficient cloud fault tolerant mapreduce"
    - "mllib machine learning in apache spark"
    - "assignment problems of different sized inputs in mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"
    - "on the computational complexity of mapreduce"

Connected through hyperedge HE-3 (induced by source paper "3lc lightweight and effective traffic compression for distributed machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "learning transferable architectures for scalable image recognition"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed machine learning via sufficient factor broadcasting"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "md gan multi discriminator generative adversarial networks for distributed datasets"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "generative adversarial parallelization"
    - "style transfer generative adversarial networks learning to play chess differently"
    - "multi generator generative adversarial nets"
    - "revisiting distributed synchronous sgd"
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "invertible conditional gans for image editing"
    - "generative multi adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "private learning on networks part ii"):
  with members:
    - "deep learning with eigenvalue decay regularizer"
    - "private learning on networks"
    - "learning privately from multiparty data"
    - "privacy preserving linear programming"
    - "distributed optimization of convex sum of non convex functions"
    - "federated optimization distributed optimization beyond the datacenter"
    - "differentially private distributed optimization"
    - "fast distributed gradient methods"

Connected through hyperedge HE-6 (induced by source paper "private learning on networks"):
  with members:
    - "privacy preserving linear programming"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed optimization for client server architecture with negative gradient weights"
    - "distributed optimization of convex sum of non convex functions"
    - "differentially private distributed online learning"
    - "privacy preservation in distributed subgradient optimization algorithms"
    - "on the privacy of optimization approaches"

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)