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: "efficient large scale graph processing on hybrid cpu and gpu systems"
  Abstract: The increasing scale and wealth of inter-connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable knowledge from large-scale graphs. However, real-world graphs are famously difficult to process efficiently. Not only they have a large memory footprint, but also most graph algorithms entail memory access patterns with poor locality, data-dependent parallelism and a low compute-to-memory access ratio. Moreover, most real-world graphs have a highly heterogeneous node degree distribution, hence partitioning these graphs for parallel processing and simultaneously achieving access locality and load-balancing is difficult. #R##N#This work starts from the hypothesis that hybrid platforms (e.g., GPU-accelerated systems) have both the potential to cope with the heterogeneous structure of real graphs and to offer a cost-effective platform for high-performance graph processing. This work assesses this hypothesis and presents an extensive exploration of the opportunity to harness hybrid systems to process large-scale graphs efficiently. In particular, (i) we present a performance model that estimates the achievable performance on hybrid platforms; (ii) informed by the performance model, we design and develop TOTEM - a processing engine that provides a convenient environment to implement graph algorithms on hybrid platforms; (iii) we show that further performance gains can be extracted using partitioning strategies that aim to produce partitions that each matches the strengths of the processing element it is allocated to, finally, (iv) we demonstrate the performance advantages of the hybrid system through a comprehensive evaluation that uses real and synthetic workloads (as large as 16 billion edges), multiple graph algorithms that stress the system in various ways, and a variety of hardware configurations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "joint service pricing and cooperative relay communication for federated learning"):
  with members:
    - "competitive data trading in wireless powered internet of things iot crowdsensing systems with blockchain"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-3 (induced by source paper "gunrock gpu graph analytics"):
  with members:
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "snap a general purpose network analysis and graph mining library"
    - "counting triangles in large graphs on gpu"
    - "mathematical foundations of the graphblas"
    - "accelerating direction optimized breadth first search on hybrid architectures"
    - "multi gpu graph analytics"
    - "emptyheaded boolean algebra based graph processing"
    - "gunrock a high performance graph processing library on the gpu"

Connected through hyperedge HE-4 (induced by source paper "multi gpu graph analytics"):
  with members:
    - "gunrock a high performance graph processing library on the gpu"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "parallel distributed breadth first search on the kepler architecture"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-5 (induced by source paper "accelerating direction optimized breadth first search on hybrid architectures"):
  with members:
    - "graphlab a new framework for parallel machine learning"

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.DC(Distributed, Parallel, and Cluster Computing)