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: "sequential detection of temporal communities by estrangement confinement"
  Abstract: Temporal communities are the result of a consistent partitioning of nodes across multiple snapshots of an evolving network, and they provide insights into how dense clusters in a network emerge, combine, split and decay over time. To reliably detect temporal communities we need to not only find a good community partition in a given snapshot but also ensure that it bears some similarity to the partition(s) found in the previous snapshot(s), a particularly difficult task given the extreme sensitivity of community structure yielded by current methods to changes in the network structure. Here, motivated by the inertia of inter-node relationships, we present a new measure of partition distance called estrangement, and show that constraining estrangement enables one to find meaningful temporal communities at various degrees of temporal smoothness in diverse real-world datasets. Estrangement confinement thus provides a principled approach to uncovering temporal communities in evolving networks.

Connected through hyperedge HE-1 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"

Connected through hyperedge HE-2 (induced by source paper "detecting community structure in dynamic social networks using the concept of leadership"):
  with members:
    - "clustering evolving networks"
    - "the bursty dynamics of the twitter information network"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "on the bursty evolution of online social networks"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "temporally evolving community detection and prediction in content centric networks"):
  with members:
    - "predicting community evolution in social networks"
    - "predicting small group accretion in social networks a topology based incremental approach"
    - "detecting overlapping temporal community structure in time evolving networks"
    - "different approaches to community evolution prediction in blogosphere"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-4 (induced by source paper "temporally evolving community detection and prediction in content centric networks"):
  with members:
    - "predicting community evolution in social networks"
    - "predicting small group accretion in social networks a topology based incremental approach"
    - "detecting overlapping temporal community structure in time evolving networks"
    - "different approaches to community evolution prediction in blogosphere"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-5 (induced by source paper "spectral algorithms for temporal graph cuts"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "on temporal graph exploration"
    - "active semi supervised learning using sampling theory for graph signals"
    - "a generalized cheeger inequality"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "is cheeger type approximation possible for nonuniform sparsest cut"
    - "a survey on multi view learning"

Connected through hyperedge HE-6 (induced by source paper "scout simultaneous time segmentation and community detection in dynamic networks"):
  with members:
    - "structure and inference in annotated networks"
    - "evaluating link prediction methods"
    - "clustering evolving networks"
    - "detecting change points in the large scale structure of evolving networks"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "dynamic networks reveal key players in aging"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "a new metric for quality of network community structure"):
  with members:
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"
    - "community detection using a neighborhood strength driven label propagation algorithm"
    - "detect overlapping and hierarchical community structure in networks"

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)