human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lstms can capture information beyond order"):
  Abstract: LSTMs have a proven track record in analyzing sequential data. But are they also useful for processing unordered instance sets? Here, we investigate the potential of LSTMs at capturing information be-yond order. We formulate the learning of the underlying structure within a set of instances using LSTM as a Multiple Instance Learning (MIL)problem. In addition, we show that LSTMs are capable of indirectly capturing instance-level information using only set-level annotations. Thus, they can be used to learn instance-level models in a weakly supervised manner. Our empirical evaluation on both simplified (MNIST) and realistic (Lookbook and Histopathology) datasets shows that the proposed method is competitive with or even surpasses state-of-the-art methods specially designed for handling MIL problems. Moreover, we show that its performance on instance-level prediction is close to that of fully-supervised method.

Includes members:
  Member "orderless recurrent models for multi label classification":
    also connected through other hyperedges:
      (none)
  Member "multiple instance learning with graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a proposal based approach for activity image to video retrieval")
  Member "deep spectral clustering using dual autoencoder network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "learning and interpreting multi multi instance learning networks":
    also connected through other hyperedges:
      (none)
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "dual motion gan for future flow embedded video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "multiple instance learning a survey of problem characteristics and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement")

Question: Please tell me which class the source paper 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)