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: "mfas multimodal fusion architecture search"
  Abstract: We tackle the problem of finding good architectures for multimodal classification problems. We propose a novel and generic search space that spans a large number of possible fusion architectures. In order to find an optimal architecture for a given dataset in the proposed search space, we leverage an efficient sequential model-based exploration approach that is tailored for the problem. We demonstrate the value of posing multimodal fusion as a neural architecture search problem by extensive experimentation on a toy dataset and two other real multimodal datasets. We discover fusion architectures that exhibit state-of-the-art performance for problems with different domain and dataset size, including the NTU RGB+D dataset, the largest multi-modal action recognition dataset available.

Connected through hyperedge HE-1 (induced by source paper "neural inheritance relation guided one shot layer assignment search"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "exploring randomly wired neural networks for image recognition"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoslim towards one shot architecture search for channel numbers"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "gimme signals discriminative signal encoding for multimodal activity recognition"):
  with members:
    - "joint activity recognition and indoor localization with wifi fingerprints"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "simitate a hybrid imitation learning benchmark"
    - "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-3 (induced by source paper "deep multi modal sets"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "supervised multimodal bitransformers for classifying images and text"
    - "roberta a robustly optimized bert pretraining approach"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "deep sets"
    - "exploring the limits of weakly supervised pretraining"
    - "rosetta large scale system for text detection and recognition in images"

Connected through hyperedge HE-4 (induced by source paper "randomnet towards fully automatic neural architecture design for multimodal learning"):
  with members:
    - "best practices for scientific research on neural architecture search"
    - "streaming end to end speech recognition for mobile devices"
    - "centralnet a multilayer approach for multimodal fusion"
    - "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-5 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "task2vec task embedding for meta learning"
    - "best sources forward domain generalization through source specific nets"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"

Connected through hyperedge HE-6 (induced by source paper "mmtm multimodal transfer module for cnn fusion"):
  with members:
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "the conversation deep audio visual speech enhancement"
    - "centralnet a multilayer approach for multimodal fusion"
    - "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"

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)