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: "best friends forever bff finding lasting dense subgraphs"
  Abstract: Graphs form a natural model for relationships and interactions between entities, for example, between people in social and cooperation networks, servers in computer networks, or tags and words in documents and tweets. But, which of these relationships or interactions are the most lasting ones? In this paper, we study the following problem: given a set of graph snapshots, which may correspond to the state of an evolving graph at different time instances, identify the set of nodes that are the most densely connected in all snapshots. We call this problem the Best Friends For Ever (BFF) problem. We provide definitions for density over multiple graph snapshots, that capture different semantics of connectedness over time, and we study the corresponding variants of the BFF problem. We then look at the On-Off BFF (O^2BFF) problem that relaxes the requirement of nodes being connected in all snapshots, and asks for the densest set of nodes in at least $k$ of a given set of graph snapshots. We show that this problem is NP-complete for all definitions of density, and we propose a set of efficient algorithms. Finally, we present experiments with synthetic and real datasets that show both the efficiency of our algorithms and the usefulness of the BFF and the O^2BFF problems.

Connected through hyperedge HE-1 (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries"):
  with members:
    - "robust densest subgraph discovery"
    - "on finding dense common subgraphs"
    - "twawler a lightweight twitter crawler"
    - "risk averse matchings over uncertain graph databases"
    - "core decomposition in multilayer networks theory algorithms and applications"
    - "robust influence maximization"
    - "robust influence maximization"
    - "streaming graph partitioning in the planted partition model"

Connected through hyperedge HE-2 (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"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "a survey of signed network mining in social media"
    - "computing maximal cliques in link streams"

Connected through hyperedge HE-3 (induced by source paper "core decomposition in multilayer networks theory algorithms and applications"):
  with members:
    - "on finding dense common subgraphs"
    - "mining maximal span cores from temporal networks"
    - "to be connected or not to be connected that is the minimum inefficiency subgraph problem"
    - "diversified coherent core search on multi layer graphs"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "a fast order based approach for core maintenance"
    - "i o efficient core graph decomposition at web scale"
    - "the minimum wiener connector problem"

Connected through hyperedge HE-4 (induced by source paper "finding events in temporal networks segmentation meets densest subgraph discovery"):
  with members:
    - "strongly polynomial efficient approximation scheme for segmentation"
    - "evidense a graph based method for finding unique high impact events with succinct keyword based descriptions"
    - "local community detection in dynamic networks"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "the bursty dynamics of the twitter information network"
    - "a novel approach to finding near cliques the triangle densest subgraph problem"

Connected through hyperedge HE-5 (induced by source paper "mining maximal span cores from temporal networks"):
  with members:
    - "enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "social resilience in online communities the autopsy of friendster"
    - "prediction of emerging technologies based on analysis of the us patent citation network"
    - "dense subgraph maintenance under streaming edge weight updates for real time story identification"
    - "listing all maximal cliques in sparse graphs in near optimal 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.SI(Social and Information Networks)