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: "distributed graphlab a framework for machine learning in the cloud"
  Abstract: While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "the non iid data quagmire of decentralized machine learning"):
  with members:
    - "group normalization"
    - "understanding batch normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (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"
    - "towards geo distributed machine learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "pipelined training with stale weights of deep convolutional neural networks"):
  with members:
    - "blink fast and generic collectives for distributed ml"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "graphse an encrypted graph database for privacy preserving social search"):
  with members:
    - "privacy preserving shortest path computation"
    - "nxgraph an efficient graph processing system on a single machine"
    - "cryptgraph privacy preserving graph analytics on encrypted graph"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "efficient authenticated data structures for graph connectivity and geometric search problems"

Connected through hyperedge HE-7 (induced by source paper "graphzero breaking symmetry for efficient graph mining"):
  with members:
    - "kaleido an efficient out of core graph mining system on a single machine"
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "graph based anomaly detection and description a survey"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-8 (induced by source paper "tigergraph a native mpp graph database"):
  with members:
    - "g core a core for future graph query languages"
    - "the gremlin graph traversal machine and language invited talk"

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.DB(Databases)