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: "see hear and read deep aligned representations"
  Abstract: We capitalize on large amounts of readily-available, synchronous data to learn a deep discriminative representations shared across three major natural modalities: vision, sound and language. By leveraging over a year of sound from video and millions of sentences paired with images, we jointly train a deep convolutional network for aligned representation learning. Our experiments suggest that this representation is useful for several tasks, such as cross-modal retrieval or transferring classifiers between modalities. Moreover, although our network is only trained with image+text and image+sound pairs, it can transfer between text and sound as well, a transfer the network never observed during training. Visualizations of our representation reveal many hidden units which automatically emerge to detect concepts, independent of the modality.

Connected through hyperedge HE-1 (induced by source paper "multimodal matching transformer for live commenting"):
  with members:
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "video description a survey of methods datasets and evaluation metrics"
    - "livebot generating live video comments based on visual and textual contexts"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "video captioning via hierarchical reinforcement learning"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "audio visual embodied navigation"):
  with members:
    - "two body problem collaborative visual task completion"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "do autonomous agents benefit from hearing"
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "co separating sounds of visual objects"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"

Connected through hyperedge HE-3 (induced by source paper "learning to localize sound sources in visual scenes analysis and applications"):
  with members:
    - "audio visual model distillation using acoustic images"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"
    - "2 5d visual sound"

Connected through hyperedge HE-4 (induced by source paper "grounding object detections with transcriptions"):
  with members:
    - "toward self supervised object detection in unlabeled videos"
    - "learning to detect and retrieve objects from unlabeled videos"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "grounded video description"
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "how2 a large scale dataset for multimodal language understanding"

Connected through hyperedge HE-5 (induced by source paper "self supervised moving vehicle tracking with stereo sound"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "the sound of motions"
    - "self supervised audio visual co segmentation"
    - "learning to localize sound source in visual scenes"
    - "variational bayesian inference for audio visual tracking of multiple speakers"
    - "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"
    - "emotion recognition in speech using cross modal transfer in the wild"

Connected through hyperedge HE-6 (induced by source paper "self supervised audio spatialization with correspondence classifier"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning blind video temporal consistency"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"
    - "audio visual scene analysis with self supervised multisensory features"
    - "objects that sound"
    - "unsupervised representation learning by sorting sequences"

Connected through hyperedge HE-7 (induced by source paper "learning a text video embedding from incomplete and heterogeneous data"):
  with members:
    - "a joint sequence fusion model for video question answering and retrieval"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning two branch neural networks for image text matching tasks"
    - "deep image prior"
    - "learnable pooling with context gating for video classification"
    - "look listen and learn"
    - "learning from video and text via large scale discriminative clustering"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-8 (induced by source paper "multilevel language and vision integration for text to clip retrieval"):
  with members:
    - "learning unsupervised visual grounding through semantic self supervision"
    - "joint event detection and description in continuous video streams"
    - "modulating early visual processing by language"
    - "objects that sound"
    - "learning two branch neural networks for image text matching tasks"
    - "single shot temporal action detection"
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"

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)