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: "nxgraph an efficient graph processing system on a single machine"
  Abstract: Recent studies show that graph processing systems on a single machine can achieve competitive performance compared with cluster-based graph processing systems. In this paper, we present NXgraph, an efficient graph processing system on a single machine. With the abstraction of vertex intervals and edge sub-shards, we propose the Destination-Sorted Sub-Shard (DSSS) structure to store a graph. By dividing vertices and edges into intervals and sub-shards, NXgraph ensures graph data access locality and enables fine-grained scheduling. By sorting edges within each sub-shard according to their destination vertices, NXgraph reduces write conflicts among different threads and achieves a high degree of parallelism. Then, three updating strategies, i.e., Single-Phase Update (SPU), Double-Phase Update (DPU), and Mixed-Phase Update (MPU), are proposed in this paper. NXgraph can adaptively choose the fastest strategy for different graph problems according to the graph size and the available memory resources to fully utilize the memory space and reduce the amount of data transfer. All these three strategies exploit streamlined disk access pattern. Extensive experiments on three real-world graphs and five synthetic graphs show that NXgraph can outperform GraphChi, TurboGraph, VENUS, and GridGraph in various situations. Moreover, NXgraph, running on a single commodity PC, can finish an iteration of PageRank on the Twitter graph with 1.5 billion edges in 2.05 seconds; while PowerGraph, a distributed graph processing system, needs 3.6s to finish the same task.

Connected through hyperedge HE-1 (induced by source paper "hygcn a gcn accelerator with hybrid architecture"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system"):
  with members:
    - "high level programming abstractions for distributed graph processing"
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "pregelix big ger graph analytics on a dataflow engine"

Connected through hyperedge HE-3 (induced by source paper "a survey on graph processing accelerators challenges and opportunities"):
  with members:
    - "flashabacus a self governing flash based accelerator for low power systems"
    - "relational inductive biases deep learning and graph networks"
    - "struc2vec learning node representations from structural identity"
    - "in datacenter performance analysis of a tensor processing unit"
    - "democratizing design for future computing platforms"
    - "graph2vec learning distributed representations of graphs"
    - "graphr accelerating graph processing using reram"
    - "novel graph processor architecture prototype system and results"

Connected through hyperedge HE-4 (induced by source paper "graph processing on fpgas taxonomy survey challenges"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "high level programming abstractions for distributed graph processing"
    - "an efficient dispatcher for large scale graphprocessing on opencl based fpgas"
    - "an efficient graph accelerator with parallel data conflict management"
    - "mathematical foundations of the graphblas"

Connected through hyperedge HE-5 (induced by source paper "graphse an encrypted graph database for privacy preserving social search"):
  with members:
    - "privacy preserving shortest path computation"
    - "cryptgraph privacy preserving graph analytics on encrypted graph"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "efficient authenticated data structures for graph connectivity and geometric search problems"

Connected through hyperedge HE-6 (induced by source paper "rept a streaming algorithm of approximating global and local triangle counts in parallel"):
  with members:
    - "on sampling from massive graph streams"
    - "ringo interactive graph analytics on big memory machines"
    - "the input output complexity of triangle enumeration"
    - "graph sample and hold a framework for big graph analytics"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "parallel triangle counting in massive streaming graphs"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "image labeling on a network using social network metadata for image classification"

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)