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: "basic performance measurements of the intel optane dc persistent memory module"
  Abstract: Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. This work comprises the first in-depth, scholarly, performance review of Intel's Optane DC PMM, exploring its capabilities as a main memory device, and as persistent, byte-addressable memory exposed to user-space applications. This report details the technologies performance under a number of modes and scenarios, and across a wide variety of macro-scale benchmarks. Optane DC PMMs can be used as large memory devices with a DRAM cache to hide their lower bandwidth and higher latency. When used in this Memory (or cached) mode, Optane DC memory has little impact on applications with small memory footprints. Applications with larger memory footprints may experience some slow-down relative to DRAM, but are now able to keep much more data in memory. When used under a file system, Optane DC PMMs can result in significant performance gains, especially when the file system is optimized to use the load/store interface of the Optane DC PMM and the application uses many small, persistent writes. For instance, using the NOVA-relaxed NVMM file system, we can improve the performance of Kyoto Cabinet by almost 2x. Optane DC PMMs can also enable user-space persistence where the application explicitly controls its writes into persistent Optane DC media. In our experiments, modified applications that used user-space Optane DC persistence generally outperformed their file system counterparts. For instance, the persistent version of RocksDB performed almost 2x faster than the equivalent program utilizing an NVMM-aware file system.

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"
    - "unimem runtime data management on non volatile memory based heterogeneous main memory"

Connected through hyperedge HE-2 (induced by source paper "dash scalable hashing on persistent memory"):
  with members:
    - "an empirical guide to the behavior and use of scalable persistent memory"
    - "persistent memory i o primitives"
    - "splitfs reducing software overhead in file systems for persistent memory"

Connected through hyperedge HE-3 (induced by source paper "a prompt report on the performance of intel optane dc persistent memory module"):
  with members:
    - "persistent memory i o primitives"
    - "the preliminary evaluation of a hypervisor based virtualization mechanism for intel optane dc persistent memory module"

Connected through hyperedge HE-4 (induced by source paper "vat asymptotic cost analysis for multi level key value stores"):
  with members:
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

Connected through hyperedge HE-5 (induced by source paper "semi asymmetric parallel graph algorithms for nvrams"):
  with members:
    - "pangolin a fault tolerant persistent memory programming library"
    - "tracking in order to recover recoverable lock free data structures"
    - "write optimized and consistent rdma based nvm systems"
    - "single machine graph analytics on massive datasets using intel optane dc persistent memory"
    - "multiversion concurrency with bounded delay and precise garbage collection"
    - "persistent memory i o primitives"
    - "persistent buffer management with optimistic consistency"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"

Connected through hyperedge HE-6 (induced by source paper "recnmp accelerating personalized recommendation with near memory processing"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "bandana using non volatile memory for storing deep learning models"
    - "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-7 (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"
    - "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"
    - "ray a distributed framework for emerging ai applications"

Connected through hyperedge HE-8 (induced by source paper "single machine graph analytics on massive datasets using intel optane dc persistent memory"):
  with members:
    - "system evaluation of the intel optane byte addressable nvm"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "bigsparse high performance external graph analytics"
    - "early evaluation of intel optane non volatile memory with hpc i o workloads"
    - "the gap benchmark suite"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

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)