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: "deep multimodal speaker naming"
  Abstract: Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online.

Connected through hyperedge HE-1 (induced by source paper "ava activespeaker an audio visual dataset for active speaker detection"):
  with members:
    - "ava speech a densely labeled dataset of speech activity in movies"
    - "the conversation deep audio visual speech enhancement"
    - "large scale visual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "audio visual scene analysis with self supervised multisensory features"
    - "automated curriculum learning for neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "an attention based speaker naming method for online adaptation in non fixed scenarios"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "attention is all you need"
    - "learning without forgetting"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "learning to separate object sounds by watching unlabeled video"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "improved speech reconstruction from silent video"
    - "supervised speech separation based on deep learning an overview"
    - "visual speech enhancement"

Connected through hyperedge HE-4 (induced by source paper "speaker naming in movies"):
  with members:
    - "a read write memory network for movie story understanding"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "end to end memory networks"
    - "skip thought vectors"
    - "movieqa understanding stories in movies through question answering"
    - "constrained speaker diarization of tv series based on visual patterns"
    - "distributed representations of words and phrases and their compositionality"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-5 (induced by source paper "a bimodal learning approach to assist multi sensory effects synchronization"):
  with members:
    - "3d convolutional neural networks for cross audio visual matching recognition"
    - "very deep convolutional neural networks for raw waveforms"
    - "soundnet learning sound representations from unlabeled video"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-6 (induced by source paper "improving speaker turn embedding by crossmodal transfer learning from face embedding"):
  with members:
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "tristounet triplet loss for speaker turn embedding"
    - "look listen and learn a multimodal lstm for speaker identification"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning face representation from scratch"
    - "constrained speaker diarization of tv series based on visual patterns"

Connected through hyperedge HE-7 (induced by source paper "self supervised vision based detection of the active speaker as a prerequisite for socially aware language acquisition"):
  with members:
    - "look listen and learn a multimodal lstm for speaker identification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "language bootstrapping learning word meanings from perception action association"

Connected through hyperedge HE-8 (induced by source paper "putting a face to the voice fusing audio and visual signals across a video to determine speakers"):
  with members:
    - "youtube 8m a large scale video classification benchmark"
    - "facenet a unified embedding for face recognition and clustering"

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.CV(Computer Vision and Pattern Recognition)