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: "temporal graph offset reconstruction towards temporally robust graph representation learning"
  Abstract: Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning that any graph used to represent them is inherently temporal. However, many of the machine learning models designed to capture knowledge about the structure of these graphs ignore this rich temporal information when creating representations of the graph. This results in models which do not perform well when used to make predictions about the future state of the graph -- especially when the delta between time stamps is not small. In this work, we explore a novel training procedure and an associated unsupervised model which creates graph representations optimised to predict the future state of the graph. We make use of graph convolutional neural networks to encode the graph into a latent representation, which we then use to train our temporal offset reconstruction method, inspired by auto-encoders, to predict a later time point -- multiple time steps into the future. Using our method, we demonstrate superior performance for the task of future link prediction compared with none-temporal state-of-the-art baselines. We show our approach to be capable of outperforming non-temporal baselines by 38% on a real world dataset.

Connected through hyperedge HE-1 (induced by source paper "temporal neighbourhood aggregation predicting future links in temporal graphs via recurrent variational graph convolutions"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "dynamic graph convolutional networks"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "e lstm d a deep learning framework for dynamic network link prediction"
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "pytorch an imperative style high performance deep learning library"
    - "gc lstm graph convolution embedded lstm for dynamic link prediction"
    - "dynamicgem a library for dynamic graph embedding methods"

Connected through hyperedge HE-2 (induced by source paper "a kings ransom for encryption ransomware classification using augmented one shot learning and bayesian approximation"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bayesian layers a module for neural network uncertainty"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "one shot learning with memory augmented neural networks"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "volenti non fit injuria ransomware and its victims"):
  with members:
    - "software defined networking based crypto ransomware detection using http traffic characteristics"
    - "on the economics of ransomware"
    - "exploit kits the production line of the cybercrime economy"
    - "node2vec scalable feature learning for networks"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "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.SI(Social and Information Networks)