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: "netsimile a scalable approach to size independent network similarity"
  Abstract: Given a set of k networks, possibly with different sizes and no overlaps in nodes or edges, how can we quickly assess similarity between them, without solving the node-correspondence problem? Analogously, how can we extract a small number of descriptive, numerical features from each graph that effectively serve as the graph's "signature"? Having such features will enable a wealth of graph mining tasks, including clustering, outlier detection, visualization, etc. #R##N#We propose NetSimile -- a novel, effective, and scalable method for solving the aforementioned problem. NetSimile has the following desirable properties: (a) It gives similarity scores that are size-invariant. (b) It is scalable, being linear on the number of edges for "signature" vector extraction. (c) It does not need to solve the node-correspondence problem. We present extensive experiments on numerous synthetic and real graphs from disparate domains, and show NetSimile's superiority over baseline competitors. We also show how NetSimile enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.

Connected through hyperedge HE-1 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats"):
  with members:
    - "poirot aligning attack behavior with kernel audit records for cyber threat hunting"
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "how powerful are graph neural networks"
    - "benchmarking crimes an emerging threat in systems security"
    - "frappuccino fault detection through runtime analysis of provenance"

Connected through hyperedge HE-2 (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-3 (induced by source paper "degree based outlier detection within ip traffic modelled as a link stream"):
  with members:
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "an algebraic approach to temporal network analysis based on temporal quantities"
    - "a unifying model for representing time varying graphs"
    - "network traffic anomaly detection"
    - "graph based anomaly detection and description a survey"
    - "temporal reachability graphs"

Connected through hyperedge HE-4 (induced by source paper "exploring the semantic content of unsupervised graph embeddings an empirical study"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "inductive representation learning on large graphs"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "learning graph topological features via gan"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-5 (induced by source paper "embeddingvis a visual analytics approach to comparative network embedding inspection"):
  with members:
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "attributed network embedding for learning in a dynamic environment"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "pixelsne visualizing fast with just enough precision via pixel aligned stochastic neighbor embedding"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "mapper comparison with wasserstein metrics"):
  with members:
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-7 (induced by source paper "a distance measure for the analysis of polar opinion dynamics in social networks"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "learning and forecasting opinion dynamics in social networks"
    - "diffusion and superposition distances for signals supported on networks"
    - "the bursty dynamics of the twitter information network"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"
    - "user level sentiment analysis incorporating social networks"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-8 (induced by source paper "fast change point detection on dynamic social networks"):
  with members:
    - "random graph models for dynamic networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "detecting weak changes in dynamic events over networks"
    - "detecting change points in the large scale structure of evolving networks"
    - "multi level anomaly detection on time varying graph data"
    - "fast generation of large scale social networks with clustering"
    - "community structure and scale free collections of erdős renyi graphs"

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)