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: "wav2pix speech conditioned face generation using generative adversarial networks"
  Abstract: Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker. In this work, we explore its potential to generate face images of a speaker by conditioning a Generative Adversarial Network (GAN) with raw speech input. We propose a deep neural network that is trained from scratch in an end-to-end fashion, generating a face directly from the raw speech waveform without any additional identity information (e.g reference image or one-hot encoding). Our model is trained in a self-supervised approach by exploiting the audio and visual signals naturally aligned in videos. With the purpose of training from video data, we present a novel dataset collected for this work, with high-quality videos of youtubers with notable expressiveness in both the speech and visual signals.

Connected through hyperedge HE-1 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "universal adversarial perturbations for speech recognition systems"
    - "mesonet a compact facial video forgery detection network"

Connected through hyperedge HE-2 (induced by source paper "3dpeople modeling the geometry of dressed humans"):
  with members:
    - "bodynet volumetric inference of 3d human body shapes"
    - "unsupervised person image synthesis in arbitrary poses"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "3d human pose estimation in the wild by adversarial learning"
    - "siclope silhouette based clothed people"
    - "talking face generation by conditional recurrent adversarial network"

Connected through hyperedge HE-3 (induced by source paper "speech2face learning the face behind a voice"):
  with members:
    - "diversity in faces"
    - "speech driven expressive talking lips with conditional sequential generative adversarial networks"
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "learnable pins cross modal embeddings for person identity"

Connected through hyperedge HE-4 (induced by source paper "text2facegan face generation from fine grained textual descriptions"):
  with members:
    - "face cap image captioning using facial expression analysis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "face2text collecting an annotated image description corpus for the generation of rich face descriptions"
    - "a style based generator architecture for generative adversarial networks"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"

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.MM(Multimedia)