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: "one weird trick for parallelizing convolutional neural networks"
  Abstract: I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "priority based parameter propagation for distributed dnn training"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"

Connected through hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "on empirical comparisons of optimizers for deep learning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "escaping saddle points with adaptive gradient methods"
    - "an exponential learning rate schedule for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"

Connected through hyperedge HE-3 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators"):
  with members:
    - "low memory neural network training a technical report"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "full deep neural network training on a pruned weight budget"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "network compression using correlation analysis of layer responses"

Connected through hyperedge HE-4 (induced by source paper "revisiting spatial invariance with low rank local connectivity"):
  with members:
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "stand alone self attention in vision models"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "you look twice gaternet for dynamic filter selection in cnns"

Connected through hyperedge HE-5 (induced by source paper "large batch training does not need warmup"):
  with members:
    - "large batch training for lstm and beyond"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "on lipschitz bounds of general convolutional neural networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "image classification at supercomputer scale"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-6 (induced by source paper "adaptive federated optimization"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "federated learning challenges methods and future directions"
    - "on the convergence of adam and beyond"
    - "the non iid data quagmire of decentralized machine learning"

Connected through hyperedge HE-7 (induced by source paper "dataset distillation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "few shot adversarial domain adaptation"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "data free knowledge distillation for deep neural networks"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "performance oriented neural architecture search"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "quenn quantization engine for low power neural networks"
    - "streaming end to end speech recognition for mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "hello edge keyword spotting on microcontrollers"
    - "optimal dnn primitive selection with partitioned boolean quadratic programming"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)