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: "found in translation learning robust joint representations by cyclic translations between modalities"
  Abstract: Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work learns joint representations by requiring all modalities as input and as a result, the learned representations may be sensitive to noisy or missing modalities at test time. With the recent success of sequence to sequence (Seq2Seq) models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time. In this paper, we propose a method to learn robust joint representations by translating between modalities. Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input. We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities. Once our translation model is trained with paired multimodal data, we only need data from the source modality at test time for final sentiment prediction. This ensures that our model remains robust from perturbations or missing information in the other modalities. We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube. Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities.

Connected through hyperedge HE-1 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding"):
  with members:
    - "human motion trajectory prediction a survey"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "on emergent communication in competitive multi agent teams"):
  with members:
    - "measuring compositionality in representation learning"
    - "on the pitfalls of measuring emergent communication"
    - "strong and simple baselines for multimodal utterance embeddings"
    - "emergence of compositional language with deep generational transmission"
    - "multimodal language analysis with recurrent multistage fusion"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "emergence of communication in an interactive world with consistent speakers"
    - "how agents see things on visual representations in an emergent language game"

Connected through hyperedge HE-3 (induced by source paper "learning representations from imperfect time series data via tensor rank regularization"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "low rank tensor completion for multiway visual data"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "tensor regression networks"
    - "tensor fusion network for multimodal sentiment analysis"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "tensor product generation networks"

Connected through hyperedge HE-4 (induced by source paper "modality to modality translation an adversarial representation learning and graph fusion network for multimodal fusion"):
  with members:
    - "strong and simple baselines for multimodal utterance embeddings"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "multimodal sentiment analysis using hierarchical fusion with context modeling"
    - "multimodal language analysis with recurrent multistage fusion"
    - "multi attention recurrent network for human communication comprehension"
    - "investigating audio visual and text fusion methods for end to end automatic personality prediction"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "efficient low rank multimodal fusion with modality specific factors"

Connected through hyperedge HE-5 (induced by source paper "multimodal transformer for unaligned multimodal language sequences"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-6 (induced by source paper "ur funny a multimodal language dataset for understanding humor"):
  with members:
    - "joint robust voicing detection and pitch estimation based on residual harmonics"
    - "detection of glottal closure instants from speech signals a quantitative review"
    - "multimodal language analysis with recurrent multistage fusion"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "modality based factorization for multimodal fusion"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "learning factorized multimodal representations"

Connected through hyperedge HE-7 (induced by source paper "m bert injecting multimodal information in the bert structure"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "modality based factorization for multimodal fusion"
    - "deep contextualized word representations"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning factorized multimodal representations"
    - "tensor fusion network for multimodal sentiment analysis"

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.LG(Machine Learning)