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: "relay a new ir for machine learning frameworks"
  Abstract: Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they be efficient, expressive, and portable across an array of heterogeneous hardware devices. These constraints are often at odds; in order to better accommodate them we propose a new high-level intermediate representation (IR) called Relay. Relay is being designed as a purely-functional, statically-typed language with the goal of balancing efficient compilation, expressiveness, and portability. We discuss the goals of Relay and highlight its important design constraints. Our prototype is part of the open source NNVM compiler framework, which powers Amazon's deep learning framework MxNet.

Connected through hyperedge HE-1 (induced by source paper "formal methods and software engineering for dl security safety and productivity for dl systems development"):
  with members:
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "towards proving the adversarial robustness of deep neural networks"
    - "output range analysis for deep neural networks"
    - "formal verification of piece wise linear feed forward neural networks"

Connected through hyperedge HE-2 (induced by source paper "polystore accelerated polystore system for heterogeneous workloads"):
  with members:
    - "rheemix in the data jungle a cross platform query optimizer"
    - "in rdbms hardware acceleration of advanced analytics"
    - "practical design space exploration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "empowering in memory relational database engines with native graph processing"
    - "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
    - "pipegen data pipe generator for hybrid analytics"
    - "the bigdawg polystore system and architecture"

Connected through hyperedge HE-3 (induced by source paper "stripe tensor compilation via the nested polyhedral model"):
  with members:
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "optimizing cnn model inference on cpus"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-4 (induced by source paper "echo compiler based gpu memory footprint reduction for lstm rnn training"):
  with members:
    - "glow graph lowering compiler techniques for neural networks"
    - "reversible recurrent neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-5 (induced by source paper "demystifying differentiable programming shift reset the penultimate backpropagator"):
  with members:
    - "glow graph lowering compiler techniques for neural networks"
    - "deep learning with dynamic computation graphs"
    - "on the fly operation batching in dynamic computation graphs"
    - "efficient implementation of a higher order language with built in ad"
    - "diffsharp an ad library for net languages"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-6 (induced by source paper "vta an open hardware software stack for deep learning"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "learning to optimize tensor programs"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "xgboost a scalable tree boosting system"
    - "eie efficient inference engine on compressed deep neural network"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-7 (induced by source paper "a hardware software blueprint for flexible deep learning specialization"):
  with members:
    - "learning to optimize tensor programs"
    - "xgboost a scalable tree boosting system"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

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.PL(Programming Languages)