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: "achieving human parity in conversational speech recognition"
  Abstract: Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where friends and family members have open-ended conversations. In both cases, our automated system establishes a new state of the art, and edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively. The key to our system's performance is the use of various convolutional and LSTM acoustic model architectures, combined with a novel spatial smoothing method and lattice-free MMI acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination.

Connected through hyperedge HE-1 (induced by source paper "a survey of adversarial learning on graphs"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "dynamic graph convolutional networks"
    - "multiscale evolutionary perturbation attack on community detection"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "time aware gradient attack on dynamic network link prediction"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "adversarial robustness of similarity based link prediction"

Connected through hyperedge HE-2 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "transfer of adversarial robustness between perturbation types"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"

Connected through hyperedge HE-3 (induced by source paper "ensembles of many diverse weak defenses can be strong defending deep neural networks against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "simple black box adversarial attacks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "enhancing robustness of machine learning systems via data transformations"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

Connected through hyperedge HE-4 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-5 (induced by source paper "adversarial sample detection for deep neural network through model mutation testing"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "spatially transformed adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "detecting adversarial samples for deep neural networks through mutation testing"
    - "towards robust training of neural networks by regularizing adversarial gradients"

Connected through hyperedge HE-6 (induced by source paper "refit a unified watermark removal framework for deep learning systems with limited data"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "strip a defence against trojan attacks on deep neural networks"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "effectiveness of distillation attack and countermeasure on neural network watermarking"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-7 (induced by source paper "improving electron micrograph signal to noise with an atrous convolutional encoder decoder"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "beating the world s best at super smash bros with deep reinforcement learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-8 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "morphological word embeddings"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice language models"
    - "neural lattice to sequence models for uncertain inputs"
    - "enriching word vectors with subword information"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"

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.CL(Computation and Language)