human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "speech2face learning the face behind a voice"):
  Abstract: How much can we infer about a person's looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of that person speaking. We design and train a deep neural network to perform this task using millions of natural Internet/YouTube videos of people speaking. During training, our model learns voice-face correlations that allow it to produce images that capture various physical attributes of the speakers such as age, gender and ethnicity. This is done in a self-supervised manner, by utilizing the natural co-occurrence of faces and speech in Internet videos, without the need to model attributes explicitly. We evaluate and numerically quantify how--and in what manner--our Speech2Face reconstructions, obtained directly from audio, resemble the true face images of the speakers.

Includes members:
  Member "diversity in faces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "wav2pix speech conditioned face generation using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples")
  Member "speech driven expressive talking lips with conditional sequential generative adversarial networks":
    also connected through other hyperedges:
      (none)
  Member "learning to localize sound source in visual scenes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "seeing voices and hearing faces cross modal biometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders")
  Member "disjoint mapping network for cross modal matching of voices and faces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "emotion recognition in speech using cross modal transfer in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic object prediction and spatial sound super resolution with binaural sounds")

Question: Please tell me which class the source paper 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)