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: "multiple instance learning with graph neural networks"
  Abstract: Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural information among instances in bags. The final graph representation is fed into a classifier for label prediction. Our algorithm is the first attempt to use GNN for MIL. We empirically show that the proposed algorithm achieves the state of the art performance on several popular MIL data sets without losing model interpretability.

Connected through hyperedge HE-1 (induced by source paper "lstms can capture information beyond order"):
  with members:
    - "orderless recurrent models for multi label classification"
    - "deep spectral clustering using dual autoencoder network"
    - "attention based deep multiple instance learning"
    - "learning and interpreting multi multi instance learning networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dual motion gan for future flow embedded video prediction"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-2 (induced by source paper "a proposal based approach for activity image to video retrieval"):
  with members:
    - "attention based deep multiple instance learning"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "semi supervised classification with graph convolutional networks"
    - "unsupervised learning of video representations using lstms"
    - "supervised hashing using graph cuts and boosted decision trees"

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)