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: "perfect match improved cross modal embeddings for audio visual synchronisation"
  Abstract: This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronization. Here, we set up the problem as one of cross-modal retrieval, where the objective is to find the most relevant audio segment given a short video clip. The method builds on the recent advances in learning representations from cross-modal self-supervision. #R##N#The main contributions of this paper are as follows: (1) we propose a new learning strategy where the embeddings are learnt via a multi-way matching problem, as opposed to a binary classification (matching or non-matching) problem as proposed by recent papers; (2) we demonstrate that performance of this method far exceeds the existing baselines on the synchronization task; (3) we use the learnt embeddings for visual speech recognition in self-supervision, and show that the performance matches the representations learnt end-to-end in a fully-supervised manner.

Connected through hyperedge HE-1 (induced by source paper "alignnet a unifying approach to audio visual alignment"):
  with members:
    - "the sound of motions"
    - "learning individual styles of conversational gesture"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "action agnostic human pose forecasting"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "dynamic temporal alignment of speech to lips"
    - "voxceleb2 deep speaker recognition"
    - "the sound of pixels"

Connected through hyperedge HE-2 (induced by source paper "naver at activitynet challenge 2019 task b active speaker detection ava"):
  with members:
    - "ava activespeaker an audio visual dataset for active speaker detection"
    - "deep audio visual speech recognition"
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "lipnet end to end sentence level lipreading"
    - "cross modal supervision for learning active speaker detection in video"
    - "adam a method for stochastic optimization"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-3 (induced by source paper "detecting adversarial attacks on audio visual speech recognition"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "characterizing audio adversarial examples using temporal dependency"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples in the physical world"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "who said that audio visual speaker diarisation of real world meetings"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "ssd single shot multibox detector"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-5 (induced by source paper "synchronising audio and ultrasound by learning cross modal embeddings"):
  with members:
    - "ultrasuite a repository of ultrasound and acoustic data from child speech therapy sessions"
    - "voxceleb2 deep speaker recognition"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "adam a method for stochastic optimization"
    - "return of the devil in the details delving deep into convolutional nets"

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)