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: "dynamic space time scheduling for gpu inference"
  Abstract: Serving deep neural networks in latency critical interactive settings often requires GPU acceleration. However, the small batch sizes typical in online inference results in poor GPU utilization, a potential performance gap which GPU resource sharing can address. In this paper, we explore several techniques to leverage both temporal and spatial multiplexing to improve GPU utilization for deep learning inference workloads. We evaluate the performance trade-offs of each approach with respect to resource-efficiency, latency predictability, and isolation when compared with conventional batched inference. Our experimental analysis suggests up to a 5x potential for improved utilization through the exploration of more advanced spatial and temporal multiplexing strategies. Our preliminary prototype of a dynamic space-time scheduler demonstrates a 3.23x floating-point throughput increase over space-only multiplexing and a 7.73x increase over time-only multiplexing for convolutions, while also providing better isolation and latency predictability.

Connected through hyperedge HE-1 (induced by source paper "modeling of deep neural network dnn placement and inference in edge computing"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "a unified optimization approach for cnn model inference on integrated gpus"
    - "on device neural net inference with mobile gpus"
    - "infaas a model less inference serving system"
    - "balancing the migration of virtual network functions with replications in data centers"

Connected through hyperedge HE-2 (induced by source paper "infaas a model less inference serving system"):
  with members:
    - "one size does not fit all quantifying and exposing the accuracy latency trade off in machine learning cloud service apis via tolerance tiers"
    - "salus fine grained gpu sharing primitives for deep learning applications"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "trims transparent and isolated model sharing for low latency deep learninginference in function as a service environments"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "lit block wise intermediate representation training for model compression"
    - "scanner efficient video analysis at scale"
    - "inferline ml inference pipeline composition framework"

Connected through hyperedge HE-3 (induced by source paper "perseus characterizing performance and cost of multi tenant serving for cnn models"):
  with members:
    - "barista efficient and scalable serverless serving system for deep learning prediction services"
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "mlperf inference benchmark"
    - "infaas a model less inference serving system"
    - "salus fine grained gpu sharing primitives for deep learning applications"
    - "auto tuning neural network quantization framework for collaborative inference between the cloud and edge"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "optimizing cnn model inference on cpus"

Connected through hyperedge HE-4 (induced by source paper "the ooo vliw jit compiler for gpu inference"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "inferline ml inference pipeline composition framework"
    - "in datacenter performance analysis of a tensor processing unit"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

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)