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: "a berkeley view of systems challenges for ai"
  Abstract: With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production. These changes have been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems software and architectures, and by the broad accessibility of these technologies. #R##N#The next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (often mission-critical) decisions on our behalf, often in highly personalized contexts. Realizing this promise, however, raises daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, that are robust against sophisticated adversaries, and that can process ever increasing amounts of data across organizations and individuals without compromising confidentiality. These challenges will be exacerbated by the end of the Moore's Law, which will constrain the amount of data these technologies can store and process. In this paper, we propose several open research directions in systems, architectures, and security that can address these challenges and help unlock AI's potential to improve lives and society.

Connected through hyperedge HE-1 (induced by source paper "large scale discrete fourier transform on tpus"):
  with members:
    - "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "a flexible framework for parallel multi dimensional dfts"
    - "high performance monte carlo simulation of ising model on tpu clusters"
    - "tensor processing units for financial monte carlo"
    - "in datacenter performance analysis of a tensor processing unit"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "p3dfft a framework for parallel computations of fourier transforms in three dimensions"

Connected through hyperedge HE-2 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"

Connected through hyperedge HE-3 (induced by source paper "strip a defence against trojan attacks on deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "model reuse attacks on deep learning systems"
    - "sentinet detecting physical attacks against deep learning systems"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"

Connected through hyperedge HE-4 (induced by source paper "ten ai stepping stones for cybersecurity"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "provable limitations of deep learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "a first look at deep learning apps on smartphones"
    - "prada protecting against dnn model stealing attacks"
    - "logan membership inference attacks against generative models"

Connected through hyperedge HE-5 (induced by source paper "declarative learning based programming as an interface to ai systems"):
  with members:
    - "tensorlog deep learning meets probabilistic dbs"
    - "dynamic routing between capsules"
    - "relational learning and feature extraction by querying over heterogeneous information networks"
    - "programming with a differentiable forth interpreter"
    - "structured factored inference a framework for automated reasoning in probabilistic programming languages"
    - "tensorflow a system for large scale machine learning"
    - "the inductive constraint programming loop"
    - "neural module networks"

Connected through hyperedge HE-6 (induced by source paper "evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support three decades development course and future prospect"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "computer aided diagnosis of lung nodule using gradient tree boosting and bayesian optimization"
    - "supervised and unsupervised tumor characterization in the deep learning era"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"

Connected through hyperedge HE-7 (induced by source paper "helen maliciously secure coopetitive learning for linear models"):
  with members:
    - "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "spectre attacks exploiting speculative execution"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "prio private robust and scalable computation of aggregate statistics"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving hierarchical clustering formal security and efficient approximation"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "privpy enabling scalable and general privacy preserving computation"
    - "cryptodl deep neural networks over encrypted data"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "privlogit efficient privacy preserving logistic regression by tailoring numerical optimizers"
    - "membership inference attacks against machine learning 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.AI(Artificial Intelligence)