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 fast analytical model of fully associative caches"
  Abstract: While the cost of computation is an easy to understand local property, the cost of data movement on cached architectures depends on global state, does not compose, and is hard to predict. As a result, programmers often fail to consider the cost of data movement. Existing cache models and simulators provide the missing information but are computationally expensive. We present a lightweight cache model for fully associative caches with least recently used (LRU) replacement policy that gives fast and accurate results. We count the cache misses without explicit enumeration of all memory accesses by using symbolic counting techniques twice: 1) to derive the stack distance for each memory access and 2) to count the memory accesses with stack distance larger than the cache size. While this technique seems infeasible in theory, due to non-linearities after the first round of counting, we show that the counting problems are sufficiently linear in practice. Our cache model often computes the results within seconds and contrary to simulation the execution time is mostly problem size independent. Our evaluation measures modeling errors below 0.6% on real hardware. By providing accurate data placement information we enable memory hierarchy aware software development.

Connected through hyperedge HE-1 (induced by source paper "polyscientist automatic loop transformations combined with microkernels for optimization of deep learning primitives"):
  with members:
    - "high performance deep learning via a single building block"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "learning to optimize tensor programs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "compiling neural networks for a computational memory accelerator"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "stripe tensor compilation via the nested polyhedral model"
    - "automated derivation of parametric data movement lower bounds for affine programs"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "a hardware software blueprint for flexible deep learning specialization"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"

Connected through hyperedge HE-3 (induced by source paper "a compiler assisted scheduler for detecting and mitigating cache based side channel attacks"):
  with members:
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "a survey of electromagnetic side channel attacks and discussion on their case progressing potential for digital forensics"
    - "eliminating timing side channel leaks using program repair"
    - "a software approach to defeating side channels in last level caches"
    - "flush flush a fast and stealthy cache attack"
    - "flush flush a fast and stealthy cache attack"

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.PF(Performance)