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: "seeing voices and hearing faces cross modal biometric matching"
  Abstract: We introduce a seemingly impossible task: given only an audio clip of someone speaking, decide which of two face images is the speaker. In this paper we study this, and a number of related cross-modal tasks, aimed at answering the question: how much can we infer from the voice about the face and vice versa? We study this task "in the wild", employing the datasets that are now publicly available for face recognition from static images (VGGFace) and speaker identification from audio (VoxCeleb). These provide training and testing scenarios for both static and dynamic testing of cross-modal matching. We make the following contributions: (i) we introduce CNN architectures for both binary and multi-way cross-modal face and audio matching, (ii) we compare dynamic testing (where video information is available, but the audio is not from the same video) with static testing (where only a single still image is available), and (iii) we use human testing as a baseline to calibrate the difficulty of the task. We show that a CNN can indeed be trained to solve this task in both the static and dynamic scenarios, and is even well above chance on 10-way classification of the face given the voice. The CNN matches human performance on easy examples (e.g. different gender across faces) but exceeds human performance on more challenging examples (e.g. faces with the same gender, age and nationality).

Connected through hyperedge HE-1 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders"):
  with members:
    - "stargan vc2 rethinking conditional methods for stargan based voice conversion"
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "text based editing of talking head video"
    - "learning individual styles of conversational gesture"
    - "speech2face learning the face behind a voice"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "learning to localize sound source in visual scenes"

Connected through hyperedge HE-2 (induced by source paper "the sound of motions"):
  with members:
    - "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
    - "self supervised audio visual co segmentation"
    - "learning to localize sound source in visual scenes"
    - "end to end learning of motion representation for video understanding"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-3 (induced by source paper "do cross modal systems leverage semantic relationships"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "learning cross modal deep embeddings for multi object image retrieval using text and sketch"
    - "learning two branch neural networks for image text matching tasks"
    - "dual path convolutional image text embedding with instance loss"
    - "visually aligned word embeddings for improving zero shot learning"
    - "look listen and learn"
    - "representations of language in a model of visually grounded speech signal"
    - "learning deep representations of medical images using siamese cnns with application to content based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "coordinated joint multimodal embeddings for generalized audio visual zeroshot classification and retrieval of videos"):
  with members:
    - "zero shot detection"
    - "learning to localize sound source in visual scenes"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-5 (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"
    - "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-6 (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"
    - "a better baseline for ava"
    - "learnable pins cross modal embeddings for person identity"
    - "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-7 (induced by source paper "icface interpretable and controllable face reenactment using gans"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "facelet bank for fast portrait manipulation"
    - "cr gan learning complete representations for multi view generation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "ganimation anatomically aware facial animation from a single image"
    - "the contextual loss for image transformation with non aligned data"
    - "which training methods for gans do actually converge"
    - "learning image to image translation using paired and unpaired training samples"

Connected through hyperedge HE-8 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals"):
  with members:
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "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"

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)