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: "representing higher order dependencies in networks"
  Abstract: To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffic or Web clickstream traffic as networks, conventional network representations that implicitly assume the Markov property (first-order dependency) can quickly become limiting. This assumption holds that, when movements are simulated on the network, the next movement depends only on the current node, discounting the fact that the movement may depend on several previous steps. However, we show that data derived from many complex systems can show up to fifth-order dependencies. In these cases, the oversimplifying assumption of the first-order network representation can lead to inaccurate network analysis results. To address this problem, we propose the higher-order network (HON) representation that can discover and embed variable orders of dependencies in a network representation. Through a comprehensive empirical evaluation and analysis, we establish several desirable characteristics of HON, including accuracy, scalability, and direct compatibility with the existing suite of network analysis methods. We illustrate how HON can be applied to a broad variety of tasks, such as random walking, clustering, and ranking, and we demonstrate that, by using it as input, HON yields more accurate results without any modification to these tasks.

Connected through hyperedge HE-1 (induced by source paper "computational human dynamics"):
  with members:
    - "joint embedding of structure and features via graph convolutional networks"
    - "location occupation and semantics based socioeconomic status inference on twitter"
    - "the temporal event graph"
    - "link transmission centrality in large scale social networks"
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "graph embedding techniques applications and performance a survey"
    - "correlations and dynamics of consumption patterns in social economic networks"
    - "stream graphs and link streams for the modeling of interactions over time"

Connected through hyperedge HE-2 (induced by source paper "network shrinkage estimation"):
  with members:
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "three hypergraph eigenvector centralities"
    - "on sampling from massive graph streams"
    - "graphlet decomposition framework algorithms and applications"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "triadic measures on graphs the power of wedge sampling"
    - "near optimal entrywise sampling for data matrices"

Connected through hyperedge HE-3 (induced by source paper "higher order visualization of causal structures in dynamics graphs"):
  with members:
    - "counting causal paths in big times series data on networks"
    - "evaluating network partitions through visualization"
    - "the mobility network of scientists analyzing temporal correlations in scientific careers"
    - "detecting path anomalies in time series data on networks"
    - "the temporal event graph"
    - "drawing dynamic graphs without timeslices"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "modelling sequences and temporal networks with dynamic community structures"

Connected through hyperedge HE-4 (induced by source paper "detecting path anomalies in time series data on networks"):
  with members:
    - "from relational data to graphs inferring significant links using generalized hypergeometric ensembles"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "keeping it short and simple summarising complex event sequences with multivariate patterns"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-5 (induced by source paper "counting causal paths in big times series data on networks"):
  with members:
    - "motifs in temporal networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "node2vec scalable feature learning for networks"
    - "temporal reachability graphs"

Connected through hyperedge HE-6 (induced by source paper "finding the right scale of a network efficient identification of causal emergence through spectral clustering"):
  with members:
    - "when the map is better than the territory"
    - "an overview of gradient descent optimization algorithms"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-7 (induced by source paper "the mobility network of scientists analyzing temporal correlations in scientific careers"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"

Connected through hyperedge HE-8 (induced by source paper "fast influencers in complex networks"):
  with members:
    - "the spread of low credibility content by social bots"

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)