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: "espresso efficient forward propagation for bcnns"
  Abstract: There are many applications scenarios for which the computational performance and memory footprint of the prediction phase of Deep Neural Networks (DNNs) needs to be optimized. Binary Neural Networks (BDNNs) have been shown to be an effective way of achieving this objective. In this paper, we show how Convolutional Neural Networks (CNNs) can be implemented using binary representations. Espresso is a compact, yet powerful library written in C/CUDA that features all the functionalities required for the forward propagation of CNNs, in a binary file less than 400KB, without any external dependencies. Although it is mainly designed to take advantage of massive GPU parallelism, Espresso also provides an equivalent CPU implementation for CNNs. Espresso provides special convolutional and dense layers for BCNNs, leveraging bit-packing and bit-wise computations for efficient execution. These techniques provide a speed-up of matrix-multiplication routines, and at the same time, reduce memory usage when storing parameters and activations. We experimentally show that Espresso is significantly faster than existing implementations of optimized binary neural networks ($\approx$ 2 orders of magnitude). Espresso is released under the Apache 2.0 license and is available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "optimizing bit serial matrix multiplication for reconfigurable computing"):
  with members:
    - "haq hardware aware automated quantization with mixed precision"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "generic and universal parallel matrix summation with a flexible compression goal for xilinx fpgas"
    - "streamlined deployment for quantized neural networks"
    - "finn a framework for fast scalable binarized neural network inference"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-2 (induced by source paper "binarized convolutional neural networks for efficient inference on gpus"):
  with members:
    - "decomposeme simplifying convnets for end to end learning"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "car type recognition with deep neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "speeding up convolutional neural networks with low rank expansions"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-3 (induced by source paper "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"):
  with members:
    - "generic and universal parallel matrix summation with a flexible compression goal for xilinx fpgas"
    - "streamlined deployment for quantized neural networks"
    - "finn a framework for fast scalable binarized neural network inference"
    - "quantized neural networks training neural networks with low precision weights and activations"

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.DC(Distributed, Parallel, and Cluster Computing)