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: "pretzel opening the black box of machine learning prediction serving systems"
  Abstract: Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5x while reducing memory footprint by 25x, and increasing throughput by 4.7x.

Connected through hyperedge HE-1 (induced by source paper "cloudburst stateful functions as a service"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "serverless computing one step forward two steps back"
    - "numpywren serverless linear algebra"
    - "ray a distributed framework for emerging ai applications"
    - "occupy the cloud distributed computing for the 99"
    - "serverless computing current trends and open problems"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-2 (induced by source paper "parity models a general framework for coding based resilience in ml inference"):
  with members:
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a code machine learning for approximate non linear coded computation"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "rafiki machine learning as an analytics service system"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "speech commands a dataset for limited vocabulary speech recognition"

Connected through hyperedge HE-3 (induced by source paper "systemds a declarative machine learning system for the end to end data science lifecycle"):
  with members:
    - "sato contextual semantic type detection in tables"
    - "holodetect few shot learning for error detection"
    - "sherlock a deep learning approach to semantic data type detection"
    - "towards federated learning at scale system design"
    - "autograph imperative style coding with graph based performance"
    - "autoaugment learning augmentation policies from data"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "incremental view maintenance with triple lock factorization benefits"

Connected through hyperedge HE-4 (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"
    - "dynamic space time scheduling for gpu inference"
    - "salus fine grained gpu sharing primitives for deep learning applications"
    - "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-5 (induced by source paper "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml"):
  with members:
    - "extending relational query processing with ml inference"
    - "machine learning at microsoft with ml net"
    - "data science through the looking glass and what we found there"
    - "query and resource optimizations a case for breaking the wall in big data systems"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "neural ordinary differential equations"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "tensorflow serving flexible high performance ml serving"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "extending relational query processing with ml inference"):
  with members:
    - "willump a statistically aware end to end optimizer for machine learning inference"
    - "machine learning at microsoft with ml net"
    - "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml"
    - "declarative recursive computation on an rdbms or why you should use a database for distributed machine learning"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "automatically leveraging mapreduce frameworks for data intensive applications"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "optimization of imperative programs in a relational database"

Connected through hyperedge HE-8 (induced by source paper "machine learning at microsoft with ml net"):
  with members:
    - "tensorflow serving flexible high performance ml serving"
    - "clipper a low latency online prediction serving system"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "distributed representations of words and phrases and their compositionality"
    - "the numpy array a structure for efficient numerical computation"

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.LG(Machine Learning)