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: "the architectural implications of facebook s dnn based personalized recommendation"
  Abstract: The widespread application of deep learning has changed the landscape of computation in the data center. In particular, personalized recommendation for content ranking is now largely accomplished leveraging deep neural networks. However, despite the importance of these models and the amount of compute cycles they consume, relatively little research attention has been devoted to systems for recommendation. To facilitate research and to advance the understanding of these workloads, this paper presents a set of real-world, production-scale DNNs for personalized recommendation coupled with relevant performance metrics for evaluation. In addition to releasing a set of open-source workloads, we conduct in-depth analysis that underpins future system design and optimization for at-scale recommendation: Inference latency varies by 60% across three Intel server generations, batching and co-location of inferences can drastically improve latency-bounded throughput, and the diverse composition of recommendation models leads to different optimization strategies.

Connected through hyperedge HE-1 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "mlperf inference benchmark"
    - "prema a predictive multi task scheduling algorithm for preemptible neural processing units"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "compositional embeddings using complementary partitions for memory efficient recommendation systems"
    - "mlperf training benchmark"
    - "benchmarking tpu gpu and cpu platforms for deep learning"

Connected through hyperedge HE-2 (induced by source paper "exploiting parallelism opportunities with deep learning frameworks"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "auto tuning tensorflow threading model for cpu backend"
    - "bandana using non volatile memory for storing deep learning models"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"):
  with members:
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "buddy compression enabling larger memory for deep learning and hpc workloads on gpus"
    - "beyond human level accuracy computational challenges in deep learning"
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "recnmp accelerating personalized recommendation with near memory processing"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "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-5 (induced by source paper "smash co designing software compression and hardware accelerated indexing for efficient sparse matrix operations"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "sparse matrix multiplication on cam based accelerator"
    - "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"
    - "graphr accelerating graph processing using reram"
    - "snap a general purpose network analysis and graph mining library"
    - "sparse 3d convolutional neural networks"
    - "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"

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)