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: "deltacon a principled massive graph similarity function"
  Abstract: How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.

Connected through hyperedge HE-1 (induced by source paper "estimating descriptors for large graphs"):
  with members:
    - "fleet butterfly estimation from a bipartite graph stream"
    - "stochastic graphlet embedding"
    - "a comprehensive survey on graph neural networks"
    - "netlsd hearing the shape of a graph"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "how powerful are graph neural networks"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "graph isomorphism in quasipolynomial time"

Connected through hyperedge HE-2 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs"):
  with members:
    - "network density of states"
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "netlsd hearing the shape of a graph"
    - "regal representation learning based graph alignment"
    - "a simple yet effective baseline for non attributed graph classification"
    - "approximating the spectrum of a graph"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "netlsd hearing the shape of a graph"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a tutorial on network embeddings"
    - "graph edit distance computation via graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-4 (induced by source paper "graph few shot learning via knowledge transfer"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "transferable end to end aspect based sentiment analysis with selective adversarial learning"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "hierarchically structured meta learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "geniepath graph neural networks with adaptive receptive paths"

Connected through hyperedge HE-5 (induced by source paper "latent network summarization bridging network embedding and summarization"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "graph summarization methods and applications a survey"

Connected through hyperedge HE-6 (induced by source paper "graph comparison via the nonbacktracking spectrum"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "spectral redemption in clustering sparse networks"
    - "social structure of facebook networks"

Connected through hyperedge HE-7 (induced by source paper "midas microcluster based detector of anomalies in edge streams"):
  with members:
    - "densealert incremental dense subtensor detection in tensor streams"
    - "less is more building selective anomaly ensembles"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-8 (induced by source paper "efficient estimation of a gromov hausdorff distance between unweighted graphs"):
  with members:
    - "computing the gromov hausdorff distance for metric trees"

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)