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: "deep multimodal learning for audio visual speech recognition"
  Abstract: In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR). First, we study an approach where uni-modal deep networks are trained separately and their final hidden layers fused to obtain a joint feature space in which another deep network is built. While the audio network alone achieves a phone error rate (PER) of $41\%$ under clean condition on the IBM large vocabulary audio-visual studio dataset, this fusion model achieves a PER of $35.83\%$ demonstrating the tremendous value of the visual channel in phone classification even in audio with high signal to noise ratio. Second, we present a new deep network architecture that uses a bilinear softmax layer to account for class specific correlations between modalities. We show that combining the posteriors from the bilinear networks with those from the fused model mentioned above results in a further significant phone error rate reduction, yielding a final PER of $34.03\%$.

Connected through hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "use what you have video retrieval using representations from collaborative experts"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "videobert a joint model for video and language representation learning"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"

Connected through hyperedge HE-2 (induced by source paper "deep audio visual speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"

Connected through hyperedge HE-3 (induced by source paper "latent variable algorithms for multimodal learning and sensor fusion"):
  with members:
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "improved speech reconstruction from silent video"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "attention is all you need"
    - "visual speech enhancement"
    - "seeing through noise visually driven speaker separation and enhancement"

Connected through hyperedge HE-4 (induced by source paper "what can computational models learn from human selective attention a review from an audiovisual crossmodal perspective"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "a self attention based deep learning method for lesion attribute detection from ct reports"
    - "enhanced robot speech recognition using biomimetic binaural sound source localization"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "cross modal attentional context learning for rgb d object detection"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "end to end saliency mapping via probability distribution prediction"

Connected through hyperedge HE-5 (induced by source paper "deep rgb d canonical correlation analysis for sparse depth completion"):
  with members:
    - "depth coefficients for depth completion"
    - "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning depth with convolutional spatial propagation network"
    - "depth aware cnn for rgb d segmentation"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-6 (induced by source paper "on single source robustness in deep fusion models"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "robust deep multi modal learning based on gated information fusion network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "state of the art speech recognition with sequence to sequence models"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mask r cnn"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "modal aware features for multimodal hashing"):
  with members:
    - "deep semantic multimodal hashing network for scalable multimedia retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "robust deep multi modal learning based on gated information fusion network"
    - "cbam convolutional block attention module"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "multimodal machine learning a survey and taxonomy"
    - "non local neural networks"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-8 (induced by source paper "analyzing utility of visual context in multimodal speech recognition under noisy conditions"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "modality attention for end to end audio visual speech recognition"
    - "how2 a large scale dataset for multimodal language understanding"
    - "visual features for context aware speech recognition"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "attention strategies for multi source sequence to sequence learning"
    - "lipnet end to end sentence level lipreading"
    - "lip reading sentences in the wild"

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.CL(Computation and Language)