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: "disjoint mapping network for cross modal matching of voices and faces"
  Abstract: We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces. Different from the existing methods, DIMNet does not explicitly learn the joint relationship between the modalities. Instead, DIMNet learns a shared representation for different modalities by mapping them individually to their common covariates. These shared representations can then be used to find the correspondences between the modalities. We show empirically that DIMNet is able to achieve better performance than other current methods, with the additional benefits of being conceptually simpler and less data-intensive.

Connected through hyperedge HE-1 (induced by source paper "speech2face learning the face behind a voice"):
  with members:
    - "diversity in faces"
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "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"
    - "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-2 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "audio visual event localization in unconstrained videos"
    - "learnable pins cross modal embeddings for person identity"
    - "git loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "on learning associations of faces and voices"
    - "look listen and learn"
    - "voxceleb a large scale speaker identification dataset"

Connected through hyperedge HE-3 (induced by source paper "reconstructing faces from voices"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "learnable pins cross modal embeddings for person identity"
    - "towards open set identity preserving face synthesis"
    - "on learning associations of faces and voices"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-4 (induced by source paper "voice face cross modal matching and retrieval a benchmark"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "arcface additive angular margin loss for deep face recognition"
    - "learnable pins cross modal embeddings for person identity"
    - "voxceleb2 deep speaker recognition"
    - "on learning associations of faces and voices"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep speaker an end to end neural speaker embedding system"

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)