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: "hitting the memory wall implications of the obvious"
  Abstract: Static cache analysis characterizes a program's cache behavior by determining in a sound but approximate manner which memory accesses result in cache hits and which result in cache misses. Such information is valuable in optimizing compilers, worst-case execution time analysis, and side-channel attack quantification and mitigation.Cache analysis is usually performed as a combination of `must' and `may' abstract interpretations, classifying instructions as either `always hit', `always miss', or `unknown'. Instructions classified as `unknown' might result in a hit or a miss depending on program inputs or the initial cache state. It is equally possible that they do in fact always hit or always miss, but the cache analysis is too coarse to see it.Our approach to eliminate this uncertainty consists in (i) a novel abstract interpretation able to ascertain that a particular instruction may definitely cause a hit and a miss on different paths, and (ii) an exact analysis, removing all remaining uncertainty, based on model checking, using abstract-interpretation results to prune down the model for scalability.We evaluated our approach on a variety of examples; it notably improves precision upon classical abstract interpretation at reasonable cost.

Connected through hyperedge HE-1 (induced by source paper "flexible communication avoiding matrix multiplication on fpga with high level synthesis"):
  with members:
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "hlslib software engineering for hardware design"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication optimal convolutional neural nets"
    - "integrated model batch and domain parallelism in training neural networks"
    - "communication lower bounds for distributed memory computations"
    - "communication lower bounds for distributed memory computations"

Connected through hyperedge HE-2 (induced by source paper "achieving multi port memory performance on single port memory with coding techniques"):
  with members:
    - "locality and availability in distributed storage"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "xoring elephants novel erasure codes for big data"
    - "on the locality of codeword symbols"

Connected through hyperedge HE-3 (induced by source paper "tlb and pagewalk performance in multicore architectures with large die stacked dram cache"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-4 (induced by source paper "progressive compressed records taking a byte out of deep learning data"):
  with members:
    - "dataset distillation"
    - "discrepancy coresets and sketches in machine learning"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "memnet memory efficiency guided neural architecture search with augment trim learning"):
  with members:
    - "dvolver efficient pareto optimal neural network architecture search"
    - "ktan knowledge transfer adversarial network"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "wearable audio and imu based shot detection in racquet sports"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "processing data where it makes sense enabling in memory computation"):
  with members:
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "architectural techniques for improving nand flash memory reliability"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "techniques for shared resource management in systems with throughput processors"
    - "errors in flash memory based solid state drives analysis mitigation and recovery"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "using ecc dram to adaptively increase memory capacity"

Connected through hyperedge HE-7 (induced by source paper "the ooo vliw jit compiler for gpu inference"):
  with members:
    - "dynamic space time scheduling for gpu inference"
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "inferline ml inference pipeline composition framework"
    - "in datacenter performance analysis of a tensor processing unit"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "near memory computing past present and future"):
  with members:
    - "memory and parallelism analysis using a platform independent approach"
    - "platform independent software analysis for near memory computing"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "identifying the potential of near data computing for apache spark"
    - "near memory address translation"
    - "performance characterization of in memory data analytics on a modern cloud server"

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)