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: "sequence segmentation using joint rnn and structured prediction models"
  Abstract: We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modules trained jointly: a recurrent neural network (RNN) module and a structured prediction model. The RNN outputs are considered as feature functions to the structured model. The overall model is trained with a structured loss function which can be designed to the given segmentation task. We demonstrate the effectiveness of our method by applying it to two simple tasks commonly used in phonetic studies: word segmentation and voice onset time segmentation. Results sug- gest the proposed model is superior to previous methods, ob- taining state-of-the-art results on the tested datasets.

Connected through hyperedge HE-1 (induced by source paper "deep spatio temporal random fields for efficient video segmentation"):
  with members:
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"
    - "semantic video cnns through representation warping"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "turning your weakness into a strength watermarking deep neural networks by backdooring"):
  with members:
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "fooling end to end speaker verification by adversarial examples"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial frontier stitching for remote neural network watermarking"
    - "mask r cnn"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-3 (induced by source paper "fooling end to end speaker verification by adversarial examples"):
  with members:
    - "practical black box attacks against machine learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "exploring the space of adversarial images"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"):
  with members:
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "neural architectures for named entity recognition"
    - "improving phoneme segmentation with recurrent neural networks"
    - "end to end learning of action detection from frame glimpses in videos"
    - "unsupervised discovery of linguistic structure including two level acoustic patterns using three cascaded stages of iterative optimization"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

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)