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: "julia a fresh approach to numerical computing"
  Abstract: Bridging cultures that have often been distant, Julia combines expertise from the diverse fields of computer science and computational science to create a new approach to numerical computing. Julia is designed to be easy and fast. Julia questions notions generally held as "laws of nature" by practitioners of numerical computing: #R##N#1. High-level dynamic programs have to be slow. #R##N#2. One must prototype in one language and then rewrite in another language for speed or deployment, and #R##N#3. There are parts of a system for the programmer, and other parts best left untouched as they are built by the experts. #R##N#We introduce the Julia programming language and its design --- a dance between specialization and abstraction. Specialization allows for custom treatment. Multiple dispatch, a technique from computer science, picks the right algorithm for the right circumstance. Abstraction, what good computation is really about, recognizes what remains the same after differences are stripped away. Abstractions in mathematics are captured as code through another technique from computer science, generic programming. #R##N#Julia shows that one can have machine performance without sacrificing human convenience.

Connected through hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey"):
  with members:
    - "shredder learning noise distributions to protect inference privacy"
    - "fusionstitching boosting execution efficiency of memory intensive computations for dl workloads"
    - "stripe tensor compilation via the nested polyhedral model"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "privacy for rescue a new testimony why privacy is vulnerable in deep models"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning of colors from color names distribution and point estimation"):
  with members:
    - "neural network with unbounded activation functions is universal approximator"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "learning to generalize to new compositions in image understanding"
    - "learning to generate compositional color descriptions"
    - "enriching word vectors with subword information"
    - "generative adversarial text to image synthesis"
    - "character sequence models for colorfulwords"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "a framework for democratizing ai"):
  with members:
    - "explainable ai for trees from local explanations to global understanding"
    - "a generic framework for privacy preserving deep learning"
    - "deep neural decision trees"
    - "auto keras an efficient neural architecture search system"
    - "tune a research platform for distributed model selection and training"
    - "ray a distributed framework for emerging ai applications"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "nested multiple instance learning in modelling of http network traffic"):
  with members:
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "discriminative models for multi instance problems with tree structure"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "predicting domain generation algorithms with long short term memory networks"
    - "using neural network formalism to solve multiple instance problems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "artificial benchmark for community detection abcd fast random graph model with community structure"):
  with members:
    - "community structure a comparative evaluation of community detection methods"
    - "the switch markov chain for sampling irregular graphs and digraphs"
    - "communication free massively distributed graph generation"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "a scalable generative graph model with community structure"
    - "are we there yet when to stop a markov chain while generating random graphs"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-6 (induced by source paper "optimizing dynamic trajectories for robustness to disturbances using polytopic projections"):
  with members:
    - "optimisation of body ground contact for augmenting whole body loco manipulation of quadruped robots"
    - "a differentiable augmented lagrangian method for bilevel nonlinear optimization"
    - "crocoddyl an efficient and versatile framework for multi contact optimal control"
    - "comparing metrics for robustness against external perturbations in dynamic trajectory optimization"
    - "application of wrench based feasibility analysis to the online trajectory optimization of legged robots"
    - "a family of iterative gauss newton shooting methods for nonlinear optimal control"
    - "on computing the shadows and slices of polytopes"

Connected through hyperedge HE-7 (induced by source paper "recursive optimization of finite blocklength allocation to mitigate age of information outage"):
  with members:
    - "real time remote estimation with hybrid arq in wireless networked control"
    - "secure short packet communications for mission critical iot applications"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "policy iteration for factored mdps"

Connected through hyperedge HE-8 (induced by source paper "dynamicppl stan like speed for dynamic probabilistic models"):
  with members:
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "pyro deep universal probabilistic programming"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

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.MS(Mathematical Software)