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: "unimem runtime data management on non volatile memory based heterogeneous main memory"
  Abstract: Non-volatile memory (NVM) provides a scalable and power-efficient solution to replace DRAM as main memory. However, because of relatively high latency and low bandwidth of NVM, NVM is often paired with DRAM to build a heterogeneous memory system (HMS). As a result, data objects of the application must be carefully placed to NVM and DRAM for best performance. In this paper, we introduce a lightweight runtime solution that automatically and transparently manage data placement on HMS without the requirement of hardware modifications and disruptive change to applications. Leveraging online profiling and performance models, the runtime characterizes memory access patterns associated with data objects, and minimizes unnecessary data movement. Our runtime solution effectively bridges the performance gap between NVM and DRAM. We demonstrate that using NVM to replace the majority of DRAM can be a feasible solution for future HPC systems with the assistance of a software-based data management.

Connected through hyperedge HE-1 (induced by source paper "demystifying the performance of hpc scientific applications on nvm based memory systems"):
  with members:
    - "single machine graph analytics on massive datasets using intel optane dc persistent memory"
    - "an empirical guide to the behavior and use of scalable persistent memory"
    - "persistent memory i o primitives"
    - "system evaluation of the intel optane byte addressable nvm"
    - "basic performance measurements of the intel optane dc persistent memory module"

Connected through hyperedge HE-2 (induced by source paper "sentinel runtime data management on heterogeneous main memorysystems for deep learning"):
  with members:
    - "multi tier buffer management and storage system design for non volatile memory"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "exascale deep learning for climate analytics"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "optimizing cnn model inference on cpus"

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)