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: "voxceleb a large scale speaker identification dataset"
  Abstract: Most existing datasets for speaker identification contain samples obtained under quite constrained conditions, and are usually hand-annotated, hence limited in size. The goal of this paper is to generate a large scale text-independent speaker identification dataset collected 'in the wild'. We make two contributions. First, we propose a fully automated pipeline based on computer vision techniques to create the dataset from open-source media. Our pipeline involves obtaining videos from YouTube; performing active speaker verification using a two-stream synchronization Convolutional Neural Network (CNN), and confirming the identity of the speaker using CNN based facial recognition. We use this pipeline to curate VoxCeleb which contains hundreds of thousands of 'real world' utterances for over 1,000 celebrities. Our second contribution is to apply and compare various state of the art speaker identification techniques on our dataset to establish baseline performance. We show that a CNN based architecture obtains the best performance for both identification and verification.

Connected through hyperedge HE-1 (induced by source paper "first order motion model for image animation"):
  with members:
    - "textured neural avatars"
    - "few shot adversarial learning of realistic neural talking head models"
    - "dwnet dense warp based network for pose guided human video generation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesture to gesture translation in the wild via category independent conditional maps"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "learning to forecast and refine residual motion for image to video generation"

Connected through hyperedge HE-2 (induced by source paper "twostreamvan improving motion modeling in video generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-3 (induced by source paper "everybody s talkin let me talk as you want"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "example guided style consistent image synthesis from semantic labeling"
    - "disentangling content and style via unsupervised geometry distillation"
    - "neural style preserving visual dubbing"
    - "make a face towards arbitrary high fidelity face manipulation"
    - "geometry aware face completion and editing"
    - "high resolution talking face generation via mutual information approximation"
    - "lip movements generation at a glance"

Connected through hyperedge HE-4 (induced by source paper "on the resilience of biometric authentication systems against random inputs"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "explainable black box attacks against model based authentication"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "voxceleb2 deep speaker recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "classification regions of deep neural networks"
    - "tacotron towards end to end speech synthesis"
    - "mitigating evasion attacks to deep neural networks via region based classification"

Connected through hyperedge HE-5 (induced by source paper "an end to end approach for the verification problem learning the right distance"):
  with members:
    - "an inductive bias for distances neural nets that respect the triangle inequality"
    - "contrastive multiview coding"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "learning non metric visual similarity for image retrieval"
    - "representation learning with contrastive predictive coding"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-6 (induced by source paper "m 3 t multi modal continuous valence arousal estimation in the wild"):
  with members:
    - "facial affect recognition in the wild using multi task learning convolutional network"
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "retinaface single stage dense face localisation in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "pytorch an imperative style high performance deep learning library"
    - "large scale visual speech recognition"

Connected through hyperedge HE-7 (induced by source paper "robust speaker recognition using speech enhancement and attention model"):
  with members:
    - "audio visual speech enhancement using conditional variational auto encoder"
    - "self multi head attention for speaker recognition"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "attention mechanism in speaker recognition what does it learn in deep speaker embedding"
    - "attention based fully convolutional network for speech emotion recognition"
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "long short term memory networks for machine reading"

Connected through hyperedge HE-8 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation learning"):
  with members:
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deep speaker embedding learning with multi level pooling for text independent speaker verification"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "a systematic study of the class imbalance problem in convolutional 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.SD(Sound)