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: "computing maximal cliques in link streams"
  Abstract: A link stream is a collection of triplets $(t, u, v)$ indicating that an interaction occurred between u and v at time t. We generalize the classical notion of cliques in graphs to such link streams: for a given $\Delta$, a $\Delta$-clique is a set of nodes and a time interval such that all pairs of nodes in this set interact at least once during each sub-interval of duration $\Delta$. We propose an algorithm to enumerate all maximal (in terms of nodes or time interval) cliques of a link stream, and illustrate its practical relevance on a real-world contact trace.

Connected through hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "a survey of community search over big graphs"
    - "finding events in temporal networks segmentation meets densest subgraph discovery"
    - "graph embedding techniques applications and performance a survey"
    - "on finding dense common subgraphs"
    - "listing all maximal k plexes in temporal graphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"

Connected through hyperedge HE-2 (induced by source paper "challenges in community discovery on temporal networks"):
  with members:
    - "olcpm an online framework for detecting overlapping communities in dynamic social networks"
    - "community discovery in dynamic networks a survey"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "the ground truth about metadata and community detection in networks"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "snap a general purpose network analysis and graph mining library"
    - "generative benchmark models for mesoscale structures in multilayer networks"

Connected through hyperedge HE-3 (induced by source paper "how fast can we reach a target vertex in stochastic temporal graphs"):
  with members:
    - "temporal vertex cover with a sliding time window"
    - "deleting edges to restrict the size of an epidemic in temporal networks"
    - "sliding window temporal graph coloring"
    - "cover time in edge uniform stochastically evolving graphs"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "temporal network optimization subject to connectivity constraints"
    - "on temporal graph exploration"
    - "dmvp foremost waypoint coverage of time varying graphs"

Connected through hyperedge HE-4 (induced by source paper "enumerating isolated cliques in temporal networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "listing all maximal k plexes in temporal graphs"
    - "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"
    - "enumerating maximal cliques in link streams with durations"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "parameterized complexity of secluded connectivity problems"

Connected through hyperedge HE-5 (induced by source paper "cores and other dense structures in complex networks"):
  with members:
    - "on finding dense common subgraphs"
    - "mining maximal span cores from temporal networks"
    - "core decomposition in multilayer networks theory algorithms and applications"
    - "best friends forever bff finding lasting dense subgraphs"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "a survey of signed network mining in social media"

Connected through hyperedge HE-6 (induced by source paper "generative temporal link prediction via self tokenized sequence modeling"):
  with members:
    - "gemsec graph embedding with self clustering"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "temporal matching"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "on the power of advice and randomization for online bipartite matching"
    - "fully polynomial time parameterized computations for graphs and matrices of low treewidth"

Connected through hyperedge HE-8 (induced by source paper "link stream graph for temporal recommendations"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"

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.DS(Data Structures and Algorithms)