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: "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
  Abstract: We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations that are zero, we target different combinations of value stream properties. We demonstrate a practical application with Bit-Tactical (TCL), a hardware accelerator which exploits weight sparsity, per layer precision variability and dynamic fine-grain precision reduction for activations, and optionally the naturally occurring sparse effectual bit content of activations to improve performance and energy efficiency. TCL benefits both sparse and dense CNNs, natively supports both convolutional and fully-connected layers, and exploits properties of all activations to reduce storage, communication, and computation demands. While TCL does not require changes to the CNN to deliver benefits, it does reward any technique that would amplify any of the aforementioned weight and activation value properties. Compared to an equivalent data-parallel accelerator for dense CNNs, TCLp, a variant of TCL improves performance by 5.05x and is 2.98x more energy efficient while requiring 22% more area.

Connected through hyperedge HE-1 (induced by source paper "beyond the memory wall a case for memory centric hpc system for deep learning"):
  with members:
    - "on scale out deep learning training for cloud and hpc"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "a read write memory network for movie story understanding"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "deep learning for video classification and captioning"

Connected through hyperedge HE-2 (induced by source paper "neummu architectural support for efficient address translations in neural processing units"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "prema a predictive multi task scheduling algorithm for preemptible neural processing units"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "beyond human level accuracy computational challenges in deep learning"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "buddy compression enabling larger memory for deep learning and hpc workloads on gpus"
    - "beyond human level accuracy computational challenges in deep learning"
    - "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "neural collaborative filtering"

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.NE(Neural and Evolutionary Computing)