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: "the power of local information in social networks"
  Abstract: We study the power of \textit{local information algorithms} for optimization problems on social networks. We focus on sequential algorithms for which the network topology is initially unknown and is revealed only within a local neighborhood of vertices that have been irrevocably added to the output set. The distinguishing feature of this setting is that locality is necessitated by constraints on the network information visible to the algorithm, rather than being desirable for reasons of efficiency or parallelizability. In this sense, changes to the level of network visibility can have a significant impact on algorithm design. #R##N#We study a range of problems under this model of algorithms with local information. We first consider the case in which the underlying graph is a preferential attachment network. We show that one can find the node of maximum degree in the network in a polylogarithmic number of steps, using an opportunistic algorithm that repeatedly queries the visible node of maximum degree. This addresses an open question of Bollob{\'a}s and Riordan. In contrast, local information algorithms require a linear number of queries to solve the problem on arbitrary networks. #R##N#Motivated by problems faced by recruiters in online networks, we also consider network coverage problems such as finding a minimum dominating set. For this optimization problem we show that, if each node added to the output set reveals sufficient information about the set's neighborhood, then it is possible to design randomized algorithms for general networks that nearly match the best approximations possible even with full access to the graph structure. We show that this level of visibility is necessary. #R##N#We conclude that a network provider's decision of how much structure to make visible to its users can have a significant effect on a user's ability to interact strategically with the network.

Connected through hyperedge HE-1 (induced by source paper "adaptive algorithm for finding connected dominating sets in uncertain graphs"):
  with members:
    - "approximation algorithm for minimum weight fault tolerant virtual backbone in unit disk graphs"
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"

Connected through hyperedge HE-2 (induced by source paper "graph based ascent algorithms for function maximization"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "random sampling of bandlimited signals on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "selective harvesting over networks"):
  with members:
    - "diversity promoting bayesian learning of latent variable models"
    - "analyzing the optimal neighborhood algorithms for budgeted and partial connected dominating set problems"
    - "building bridges viewing active learning from the multi armed bandit lens"
    - "bayesian optimal active search and surveying"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "importance weighted active learning"

Connected through hyperedge HE-4 (induced by source paper "local computation algorithms for graphs of non constant degrees"):
  with members:
    - "flow based algorithms for local graph clustering"
    - "distributed maximum matching in bounded degree graphs"
    - "new techniques and tighter bounds for local computation algorithms"
    - "best of two local models local centralized and local distributed algorithms"
    - "local algorithms for sparse spanning graphs"
    - "a local computation approximation scheme to maximum matching"
    - "a simple online competitive adaptation of lempel ziv compression with efficient random access support"
    - "local computation mechanism design"

Connected through hyperedge HE-5 (induced by source paper "information gathering in networks via active exploration"):
  with members:
    - "crowdstar a social task routing framework for online communities"
    - "search in power law networks"

Connected through hyperedge HE-6 (induced by source paper "quick detection of high degree entities in large directed networks"):
  with members:
    - "quick detection of nodes with large degrees"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-7 (induced by source paper "analyzing the optimal neighborhood algorithms for budgeted and partial connected dominating set problems"):
  with members:
    - "improved approximation algorithms for budgeted node weighted steiner problems"
    - "online myopic network covering"
    - "scheduling with outliers"

Connected through hyperedge HE-8 (induced by source paper "maximum coverage and maximum connected covering in social networks with partial topology information"):
  with members:
    - "online myopic network covering"

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)