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: "discriminative segmental cascades for feature rich phone recognition"
  Abstract: Discriminative segmental models, such as segmental conditional random fields (SCRFs) and segmental structured support vector machines (SSVMs), have had success in speech recognition via both lattice rescoring and first-pass decoding. However, such models suffer from slow decoding, hampering the use of computationally expensive features, such as segment neural networks or other high-order features. A typical solution is to use approximate decoding, either by beam pruning in a single pass or by beam pruning to generate a lattice followed by a second pass. In this work, we study discriminative segmental models trained with a hinge loss (i.e., segmental structured SVMs). We show that beam search is not suitable for learning rescoring models in this approach, though it gives good approximate decoding performance when the model is already well-trained. Instead, we consider an approach inspired by structured prediction cascades, which use max-marginal pruning to generate lattices. We obtain a high-accuracy phonetic recognition system with several expensive feature types: a segment neural network, a second-order language model, and second-order phone boundary features.

Connected through hyperedge HE-1 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-2 (induced by source paper "classification with joint time frequency scattering"):
  with members:
    - "scaling the scattering transform deep hybrid networks"
    - "look listen and learn"
    - "basic filters for convolutional neural networks training or design"
    - "soundnet learning sound representations from unlabeled video"
    - "deep learning in neural networks"
    - "deep scattering spectrum"
    - "speech recognition with deep recurrent neural networks"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "sequence prediction with neural segmental models"):
  with members:
    - "optimizing expected word error rate via sampling for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "end to end training approaches for discriminative segmental models"
    - "segmental recurrent neural networks for end to end speech recognition"

Connected through hyperedge HE-4 (induced by source paper "end to end neural segmental models for speech recognition"):
  with members:
    - "direct acoustics to word models for english conversational speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "end to end training approaches for discriminative segmental models"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "efficient segmental cascades for speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-5 (induced by source paper "segmental recurrent neural networks for end to end speech recognition"):
  with members:
    - "listen attend and spell"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "attention based models for speech recognition"
    - "segmental recurrent neural networks"
    - "deep speech scaling up end to end speech recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "end to end training approaches for discriminative segmental models"):
  with members:
    - "sequence to sequence learning as beam search optimization"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "on the sample complexity of end to end training vs semantic abstraction training"
    - "efficient segmental cascades for speech recognition"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "lexicon free fingerspelling recognition from video data models and signer adaptation"):
  with members:
    - "signer independent fingerspelling recognition with deep neural network adaptation"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "theano a python framework for fast computation of mathematical expressions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "american sign language fingerspelling recognition from video methods for unrestricted recognition and signer independence"):
  with members:
    - "signer independent fingerspelling recognition with deep neural network adaptation"
    - "very deep convolutional networks for large scale image recognition"
    - "frustratingly easy domain adaptation"

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)