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: "learnable pins cross modal embeddings for person identity"
  Abstract: We propose and investigate an identity sensitive joint embedding of face and voice. Such an embedding enables cross-modal retrieval from voice to face and from face to voice. We make the following four contributions: first, we show that the embedding can be learnt from videos of talking faces, without requiring any identity labels, using a form of cross-modal self-supervision; second, we develop a curriculum learning schedule for hard negative mining targeted to this task, that is essential for learning to proceed successfully; third, we demonstrate and evaluate cross-modal retrieval for identities unseen and unheard during training over a number of scenarios and establish a benchmark for this novel task; finally, we show an application of using the joint embedding for automatically retrieving and labelling characters in TV dramas.

Connected through hyperedge HE-1 (induced by source paper "adversarial representation learning for text to image matching"):
  with members:
    - "d sne domain adaptation using stochastic neighborhood embedding"
    - "mirrorgan learning text to image generation by redescription"
    - "in defense of the triplet loss for visual recognition"
    - "boosting standard classification architectures through a ranking regularizer"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "deep imbalanced attribute classification using visual attention aggregation"

Connected through hyperedge HE-2 (induced by source paper "use what you have video retrieval using representations from collaborative experts"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "cross task weakly supervised learning from instructional videos"
    - "on the variance of the adaptive learning rate and beyond"
    - "videobert a joint model for video and language representation learning"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "massively parallel hyperparameter tuning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "ghostvlad for set based face recognition"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "epic fusion audio visual temporal binding for egocentric action recognition"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "a better baseline for ava"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "2 5d visual sound"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-5 (induced by source paper "video face clustering with unknown number of clusters"):
  with members:
    - "centroid based deep metric learning for speaker recognition"
    - "self supervised learning of face representations for video face clustering"
    - "tvqa localized compositional video question answering"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (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"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "audio visual event localization in unconstrained videos"
    - "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-7 (induced by source paper "reconstructing faces from voices"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "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-8 (induced by source paper "voice face cross modal matching and retrieval a benchmark"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "arcface additive angular margin loss for deep face recognition"
    - "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)