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: "centralnet a multilayer approach for multimodal fusion"
  Abstract: This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past multimodal approaches either work by projecting the features of different modalities into the same space, or by coordinating the representations of each modality through the use of constraints, our approach borrows from both visions. More specifically, assuming each modality can be processed by a separated deep convolutional network, allowing to take decisions independently from each modality, we introduce a central network linking the modality specific networks. This central network not only provides a common feature embedding but also regularizes the modality specific networks through the use of multi-task learning. The proposed approach is validated on 4 different computer vision tasks on which it consistently improves the accuracy of existing multimodal fusion approaches.

Connected through hyperedge HE-1 (induced by source paper "randomnet towards fully automatic neural architecture design for multimodal learning"):
  with members:
    - "mfas multimodal fusion architecture search"
    - "best practices for scientific research on neural architecture search"
    - "streaming end to end speech recognition for mobile devices"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "truly multi modal youtube 8m video classification with video audio and text"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "mmtm multimodal transfer module for cnn fusion"):
  with members:
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "mfas multimodal fusion architecture search"
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "view adaptive neural networks for high performance skeleton based human action recognition"

Connected through hyperedge HE-3 (induced by source paper "mfas multimodal fusion architecture search"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "efficient progressive neural architecture search"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "multi level sensor fusion with deep learning"):
  with members:
    - "moddrop adaptive multi modal gesture recognition"

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.AI(Artificial Intelligence)