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: "boinc a platform for volunteer computing"
  Abstract: Volunteer computing is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe its features, architecture, and implementation.

Connected through hyperedge HE-1 (induced by source paper "jsdoop and tensorflow js volunteer distributed web browser based neural network training"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "towards federated learning at scale system design"
    - "moving deep learning into web browser how far can we go"
    - "deep learning towards mobile applications"
    - "deep learning in mobile and wireless networking a survey"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-2 (induced by source paper "pando a volunteer computing platform for the web"):
  with members:
    - "not so fast analyzing the performance of webassembly vs native code"
    - "genet a quickly scalable fat tree overlay for personal volunteer computing using webrtc"
    - "personal volunteer computing"
    - "mlitb machine learning in the browser"
    - "cloud computing and grid computing 360 degree compared"
    - "the anatomy of the grid enabling scalable virtual organizations"

Connected through hyperedge HE-3 (induced by source paper "pando personal volunteer computing in browsers"):
  with members:
    - "not so fast analyzing the performance of webassembly vs native code"
    - "genet a quickly scalable fat tree overlay for personal volunteer computing using webrtc"
    - "personal volunteer computing"
    - "mlitb machine learning in the browser"

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)