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 "deep signature transforms"):
  Abstract: The signature is an infinite graded sequence of statistics known to characterise a stream of data up to a negligible equivalence class. It is a transform which has previously been treated as a fixed feature transformation, on top of which a model may be built. We propose a novel approach which combines the advantages of the signature transform with modern deep learning frameworks. By learning an augmentation of the stream prior to the signature transform, the terms of the signature may be selected in a data-dependent way. More generally, we describe how the signature transform may be used as a layer anywhere within a neural network. In this context it may be interpreted as a pooling operation. We present the results of empirical experiments to back up the theoretical justification. Code available at this https URL.

Includes members:
  Member "signatory differentiable computations of the signature and logsignature transforms on both cpu and gpu":
    also connected through other hyperedges:
      (none)
  Member "the iisignature library efficient calculation of iterated integral signatures and log signatures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "signatory differentiable computations of the signature and logsignature transforms on both cpu and gpu")
  Member "developing the path signature methodology and its application to landmark based human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning stochastic differential equations using rnn with log signature features")
  Member "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aggregation cross entropy for sequence recognition")
  Member "dropsample a new training method to enhance deep convolutional neural networks for large scale unconstrained handwritten chinese character recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking deep active learning using unlabeled data at model training")
  Member "generative moment matching networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deepwriterid an end to end online text independent writer identification system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "signatory differentiable computations of the signature and logsignature transforms on both cpu and gpu")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.LG(Machine Learning)