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: "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
  Abstract: Studying IP traffic is crucial for many applications. We focus here on the detection of (structurally and temporally) dense sequences of interactions, that may indicate botnets or coordinated network scans. More precisely, we model a MAWI capture of IP traffic as a link streams, i.e. a sequence of interactions $(t_1 , t_2 , u, v)$ meaning that devices $u$ and $v$ exchanged packets from time $t_1$ to time $t_2$ . This traffic is captured on a single router and so has a bipartite structure: links occur only between nodes in two disjoint sets. We design a method for finding interesting bipartite cliques in such link streams, i.e. two sets of nodes and a time interval such that all nodes in the first set are linked to all nodes in the second set throughout the time interval. We then explore the bipartite cliques present in the considered trace. Comparison with the MAWILab classification of anomalous IP addresses shows that the found cliques succeed in detecting anomalous network activity.

Connected through hyperedge HE-1 (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"
    - "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"
    - "netsimile a scalable approach to size independent network similarity"

Connected through hyperedge HE-2 (induced by source paper "weighted bipartite or directed stream graphs for the modeling of temporal networks"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"
    - "on multiaspect graphs"
    - "a unifying model for representing time varying graphs"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "a tutorial on clique problems in communications and signal processing"):
  with members:
    - "eptas for max clique on disks and unit balls"
    - "enhanced uplink resource allocation in non orthogonal multiple access systems"
    - "delivery time reduction for order constrained applications using binary network codes"
    - "data dissemination using instantly decodable binary codes in fog radio access networks"
    - "on multiaspect graphs"
    - "distributed hybrid scheduling in multi cloud networks using conflict graphs"
    - "resilient backhaul network design using hybrid radio free space optical technology"
    - "rate aware instantly decodable network codes"

Connected through hyperedge HE-4 (induced by source paper "stream graphs and link streams for the modeling of interactions over time"):
  with members:
    - "degree based outlier detection within ip traffic modelled as a link stream"
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "movie rating prediction using content based and link stream features"
    - "motifs in temporal networks"
    - "time series analysis of temporal networks"
    - "graphprints towards a graph analytic method for network anomaly detection"
    - "semirings for temporal network analysis"
    - "on multiaspect graphs"

Connected through hyperedge HE-5 (induced by source paper "enumerating maximal cliques in link streams with durations"):
  with members:
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "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.DS(Data Structures and Algorithms)