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: "modeling time series similarity with siamese recurrent networks"
  Abstract: Traditional techniques for measuring similarities between time series are based on handcrafted similarity measures, whereas more recent learning-based approaches cannot exploit external supervision. We combine ideas from time-series modeling and metric learning, and study siamese recurrent networks (SRNs) that minimize a classification loss to learn a good similarity measure between time series. Specifically, our approach learns a vectorial representation for each time series in such a way that similar time series are modeled by similar representations, and dissimilar time series by dissimilar representations. Because it is a similarity prediction models, SRNs are particularly well-suited to challenging scenarios such as signature recognition, in which each person is a separate class and very few examples per class are available. We demonstrate the potential merits of SRNs in within-domain and out-of-domain classification experiments and in one-shot learning experiments on tasks such as signature, voice, and sign language recognition.

Connected through hyperedge HE-1 (induced by source paper "learn to generate time series conditioned graphs with generative adversarial nets"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "labeled graph generative adversarial networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "deep graph translation"
    - "junction tree variational autoencoder for molecular graph generation"
    - "simple recurrent units for highly parallelizable recurrence"

Connected through hyperedge HE-2 (induced by source paper "meta learning for few shot time series classification"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "unsupervised scalable representation learning for multivariate time series"
    - "deep learning for time series classification a review"
    - "a cnn adapted to time series for the classification of supernovae"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "autowarp learning a warping distance from unlabeled time series using sequence autoencoders"
    - "transfer learning for time series classification"

Connected through hyperedge HE-3 (induced by source paper "osvnet convolutional siamese network for writer independent online signature verification"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "attention based few shot person re identification using meta learning"):
  with members:
    - "features for multi target multi camera tracking and re identification"
    - "attention aware compositional network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "learning to remember rare events"
    - "end to end comparative attention networks for person re identification"

Connected through hyperedge HE-5 (induced by source paper "human motion analysis with deep metric learning"):
  with members:
    - "lstm a search space odyssey"
    - "learning feature pyramids for human pose estimation"
    - "no fuss distance metric learning using proxies"
    - "learning spread out local feature descriptors"
    - "compositional human pose regression"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "autowarp learning a warping distance from unlabeled time series using sequence autoencoders"):
  with members:
    - "clinically meaningful comparisons over time an approach to measuring patient similarity based on subsequence alignment"
    - "timenet pre trained deep recurrent neural network for time series classification"
    - "semi supervised sequence learning"
    - "learning to diagnose with lstm recurrent neural networks"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-7 (induced by source paper "neuralwarp time series similarity with warping networks"):
  with members:
    - "can recurrent neural networks warp time"
    - "learning to compare image patches via convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "metric learning for temporal sequence alignment"
    - "learning fine grained image similarity with deep ranking"
    - "learning content similarity for music recommendation"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-8 (induced by source paper "dynamic state warping"):
  with members:
    - "the great time series classification bake off an experimental evaluation of recently proposed algorithms extended version"
    - "metric learning for temporal sequence alignment"
    - "unsupervised learning of spatiotemporally coherent metrics"
    - "distributed representations of words and phrases and their compositionality"

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.CV(Computer Vision and Pattern Recognition)