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 representation learning from temporal data"
  Abstract: In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video, audio and sensor signals, it becomes imperative to consider their temporal structure during the fusion process. In this paper, we propose the Correlational Recurrent Neural Network (CorrRNN), a novel temporal fusion model for fusing multiple input modalities that are inherently temporal in nature. Key features of our proposed model include: (i) simultaneous learning of the joint representation and temporal dependencies between modalities, (ii) use of multiple loss terms in the objective function, including a maximum correlation loss term to enhance learning of cross-modal information, and (iii) the use of an attention model to dynamically adjust the contribution of different input modalities to the joint representation. We validate our model via experimentation on two different tasks: video- and sensor-based activity classification, and audio-visual speech recognition. We empirically analyze the contributions of different components of the proposed CorrRNN model, and demonstrate its robustness, effectiveness and state-of-the-art performance on multiple datasets.

Connected through hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-3 (induced by source paper "temporal multimodal fusion for driver behavior prediction tasks using gated recurrent fusion units"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "dual modality seq2seq network for audio visual event localization"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "audio visual event localization in unconstrained videos"
    - "the sound of pixels"
    - "efficient large scale multi modal classification"
    - "objects that sound"
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "look listen and learn"

Connected through hyperedge HE-5 (induced by source paper "harmonized multimodal learning with gaussian process latent variable models"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "training and evaluating multimodal word embeddings with large scale web annotated images"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "variational auto encoded deep gaussian processes"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-6 (induced by source paper "audio visual event localization in unconstrained videos"):
  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 to separate object sounds by watching unlabeled video"
    - "cross modal embeddings for video and audio retrieval"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "efficient large scale multi modal classification"
    - "objects that sound"

Connected through hyperedge HE-7 (induced by source paper "cross and learn cross modal self supervision"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep unsupervised learning of visual similarities"
    - "colorization as a proxy task for visual understanding"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "the kinetics human action video dataset"
    - "unsupervised representation learning by sorting sequences"
    - "representation learning by learning to count"

Connected through hyperedge HE-8 (induced by source paper "state representation learning for control an overview"):
  with members:
    - "decoupling dynamics and reward for transfer learning"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "deep reinforcement learning that matters"
    - "progressive neural networks"
    - "independently controllable factors"
    - "interactive perception leveraging action in perception and perception in action"
    - "pves position velocity encoders for unsupervised learning of structured state representations"

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)