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: "concept drift and anomaly detection in graph streams"
  Abstract: Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains. Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes. The methodology is general and considers a process generating attributed graphs with a variable number of vertices/edges, without the need to assume a one-to-one correspondence between vertices at different time steps. The methodology acts by embedding every graph of the stream into a vector domain, where a conventional multivariate change detection procedure can be easily applied. We ground the soundness of our proposal by proving several theoretical results. In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings. Experimental results are contrasted with respect to suitable baseline methods, demonstrating the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "autoregressive models for sequences of graphs"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "relational inductive biases deep learning and graph networks"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "discovering objects and their relations from entangled scene representations"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "learning aligned spatial graph convolutional networks for graph classification"):
  with members:
    - "anonymous walk embeddings"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "generalizing the convolution operator to extend cnns to irregular domains"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-4 (induced by source paper "learning vertex convolutional networks for graph classification"):
  with members:
    - "learning convolutional neural networks for graphs"
    - "generalizing the convolution operator to extend cnns to irregular domains"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "diffusion convolutional neural networks"
    - "show and tell a neural image caption generator"
    - "spectral networks and locally connected networks on graphs"
    - "learning by transduction"

Connected through hyperedge HE-5 (induced by source paper "learning backtrackless aligned spatial graph convolutional networks for graph classification"):
  with members:
    - "anonymous walk embeddings"
    - "learning convolutional neural networks for graphs"
    - "generalizing the convolution operator to extend cnns to irregular domains"
    - "deep convolutional networks on graph structured data"
    - "diffusion convolutional neural networks"
    - "spectral networks and locally connected networks on graphs"
    - "learning by transduction"

Connected through hyperedge HE-6 (induced by source paper "detecting sequences of system states in temporal networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "fast change point detection on dynamic social networks"
    - "measuring large scale social networks with high resolution"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"
    - "modeling complex systems with adaptive networks"

Connected through hyperedge HE-7 (induced by source paper "online multivariate anomaly detection and localization for high dimensional settings"):
  with members:
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "anomaly detection with score functions based on nearest neighbor graphs"

Connected through hyperedge HE-8 (induced by source paper "graph convolutional neural networks based on quantum vertex saliency"):
  with members:
    - "tree based kernel for graphs with continuous attributes"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "generalizing the convolution operator to extend cnns to irregular domains"

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)