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: "exploring the structure and function of temporal networks with dynamic graphlets"
  Abstract: With the growing amount of available temporal real-world network data, an important question is how to efficiently study these data. One can simply model a temporal network as either a single aggregate static network, or as a series of time-specific snapshots, each of which is an aggregate static network over the corresponding time window. The advantage of modeling the temporal data in these two ways is that one can use existing well established methods for static network analysis to study the resulting aggregate network(s). Here, we develop a novel approach for studying temporal network data more explicitly. We base our methodology on the well established notion of graphlets (subgraphs), which have been successfully used in numerous contexts in static network research. Here, we take the notion of static graphlets to the next level and develop new theory needed to allow for graphlet-based analysis of temporal networks. Our new notion of dynamic graphlets is quite different than existing approaches for dynamic network analysis that are based on temporal motifs (statistically significant subgraphs). Namely, these approaches suffer from many limitations. For example, they can only deal with subgraph structures of limited complexity. Also, their major drawback is that their results heavily depend on the choice of a null network model that is required to evaluate the significance of a subgraph. However, choosing an appropriate null network model is a non-trivial task. Our dynamic graphlet approach overcomes the limitations of the existing temporal motif-based approaches. At the same time, when we thoroughly evaluate the ability of our new approach to characterize the structure and function of an entire temporal network or of individual nodes, we find that the dynamic graphlet approach outperforms the static graphlet approach, which indicates that accounting for temporal information helps.

Connected through hyperedge HE-1 (induced by source paper "item independent temporal motifs to summarize and compare temporal networks"):
  with members:
    - "understanding information flow in cascades using network motifs"
    - "community interaction and conflict on the web"
    - "a sampling framework for counting temporal motifs"
    - "motifs in temporal networks"
    - "higher order organization of complex networks"
    - "an introduction to temporal graphs an algorithmic perspective"

Connected through hyperedge HE-2 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "butterfly counting in bipartite networks"
    - "estimation of graphlet statistics"
    - "combinatorial algorithm for counting small induced graphs and orbits"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous network motifs"):
  with members:
    - "higher order spectral clustering for heterogeneous graphs"
    - "on sampling from massive graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "graphlet decomposition framework algorithms and applications"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-4 (induced by source paper "the power of dynamic social networks to predict individuals mental health"):
  with members:
    - "heterogeneous network approach to predict individuals mental health"
    - "social network structure is predictive of health and wellness"
    - "daily stress recognition from mobile phone data weather conditions and individual traits"
    - "dynamic networks reveal key players in aging"
    - "happiness is assortative in online social networks"

Connected through hyperedge HE-5 (induced by source paper "a sampling framework for counting temporal motifs"):
  with members:
    - "a chronological edge driven approach to temporal subgraph isomorphism"
    - "motifs in temporal networks"
    - "enumerating maximal cliques in link streams with durations"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-6 (induced by source paper "graphlets versus node2vec and struc2vec in the task of network alignment"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "predicting multicellular function through multi layer tissue networks"
    - "a survey on network embedding"
    - "alignment of dynamic networks"
    - "node2vec scalable feature learning for networks"
    - "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs"
    - "evaluating link prediction methods"
    - "clustering and community detection in directed networks a survey"

Connected through hyperedge HE-7 (induced by source paper "using compressed suffix arrays for a compact representation of temporal graphs"):
  with members:
    - "action2activity recognizing complex activities from sensor data"
    - "compact trip representation over networks"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "windows into relational events data structures for contiguous subsequences of edges"
    - "efficient snapshot retrieval over historical graph data"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-8 (induced by source paper "on the enumeration of maximal delta gamma cliques of a temporal network"):
  with members:
    - "enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "mining maximal cliques from an uncertain graph"
    - "listing all maximal cliques in large sparse real world 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)