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: "dense prediction on sequences with time dilated convolutions for speech recognition"
  Abstract: In computer vision pixelwise dense prediction is the task of predicting a label for each pixel in the image. Convolutional neural networks achieve good performance on this task, while being computationally efficient. In this paper we carry these ideas over to the problem of assigning a sequence of labels to a set of speech frames, a task commonly known as framewise classification. We show that dense prediction view of framewise classification offers several advantages and insights, including computational efficiency and the ability to apply batch normalization. When doing dense prediction we pay specific attention to strided pooling in time and introduce an asymmetric dilated convolution, called time-dilated convolution, that allows for efficient and elegant implementation of pooling in time. We show results using time-dilated convolutions in a very deep VGG-style CNN with batch normalization on the Hub5 Switchboard-2000 benchmark task. With a big n-gram language model, we achieve 7.7% WER which is the best single model single-pass performance reported so far.

Connected through hyperedge HE-1 (induced by source paper "multi representation knowledge distillation for audio classification"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "paraphrasing complex network network compression via factor transfer"
    - "weakly supervised crnn system for sound event detection with large scale unlabeled in domain data"
    - "large scale distributed neural network training through online distillation"
    - "sample mixed based data augmentation for domestic audio tagging"
    - "deep mutual learning"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-3 (induced by source paper "modeling the past and future contexts for session based recommendation"):
  with members:
    - "deep learning based sequential recommender systems concepts algorithms and evaluations"
    - "mass masked sequence to sequence pre training for language generation"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "cosrec 2d convolutional neural networks for sequential recommendation"
    - "hierarchical gating networks for sequential recommendation"
    - "a survey on session based recommender systems"
    - "adversarial personalized ranking for recommendation"

Connected through hyperedge HE-4 (induced by source paper "score and lyrics free singing voice generation"):
  with members:
    - "dilated convolution with dilated gru for music source separation"
    - "data efficient voice cloning for neural singing synthesis"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "gansynth adversarial neural audio synthesis"
    - "an overview of lead and accompaniment separation in music"
    - "group normalization"
    - "revisiting singing voice detection a quantitative review and the future outlook"

Connected through hyperedge HE-5 (induced by source paper "dilated convolution with dilated gru for music source separation"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "feudal networks for hierarchical reinforcement learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale multi band densenets for audio source separation"

Connected through hyperedge HE-6 (induced by source paper "big little net an efficient multi scale feature representation for visual and speech recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "cbam convolutional block attention module"
    - "skipnet learning dynamic routing in convolutional networks"
    - "progressive neural architecture search"
    - "english conversational telephone speech recognition by humans and machines"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-7 (induced by source paper "a multiversion programming inspired approach to detecting audio adversarial examples"):
  with members:
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "audio adversarial examples targeted attacks on speech to text"
    - "generalizable adversarial examples detection based on bi model decision mismatch"

Connected through hyperedge HE-8 (induced by source paper "a simple convolutional generative network for next item recommendation"):
  with members:
    - "adversarial personalized ranking for recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "convolutional sequence to sequence learning"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

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)