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: "on the efficiency of the information networks in social media"
  Abstract: Social media sites are information marketplaces, where users produce and consume a wide variety of information and ideas. In these sites, users typically choose their information sources, which in turn determine what specific information they receive, how much information they receive and how quickly this information is shown to them. In this context, a natural question that arises is how efficient are social media users at selecting their information sources. In this work, we propose a computational framework to quantify users' efficiency at selecting information sources. Our framework is based on the assumption that the goal of users is to acquire a set of unique pieces of information. To quantify user's efficiency, we ask if the user could have acquired the same pieces of information from another set of sources more efficiently. We define three different notions of efficiency -- link, in-flow, and delay -- corresponding to the number of sources the user follows, the amount of (redundant) information she acquires and the delay with which she receives the information. Our definitions of efficiency are general and applicable to any social media system with an underlying in- formation network, in which every user follows others to receive the information they produce.   In our experiments, we measure the efficiency of Twitter users at acquiring different types of information. We find that Twitter users exhibit sub-optimal efficiency across the three notions of efficiency, although they tend to be more efficient at acquiring non- popular pieces of information than they are at acquiring popular pieces of information. We then show that this lack of efficiency is a consequence of the triadic closure mechanism by which users typically discover and follow other users in social media. Thus, our study reveals a tradeoff between the efficiency and discoverability of information sources. Finally, we develop a heuristic algorithm that enables users to be significantly more efficient at acquiring the same unique pieces of information.

Connected through hyperedge HE-1 (induced by source paper "on the origins of memes by means of fringe web communities"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "memesequencer sparse matching for embedding image macros"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "information evolution in social networks"
    - "tensorflow a system for large scale machine learning"
    - "predicting successful memes using network and community structure"
    - "clustering memes in social media"
    - "truthy mapping the spread of astroturf in microblog streams"

Connected through hyperedge HE-2 (induced by source paper "limited individual attention and online virality of low quality information"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "the rise of social bots"
    - "vip incorporating human cognitive biases in a probabilistic model of retweeting"
    - "measuring online social bubbles"
    - "the role of information diffusion in the evolution of social networks"
    - "partisan asymmetries in online political activity"
    - "novelty and collective attention"

Connected through hyperedge HE-3 (induced by source paper "network structure inference a survey motivations methods and applications"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network model selection using task focused minimum description length"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "network model selection for task focused attributed network inference"
    - "evaluating social networks using task focused network inference"
    - "inferring networks of substitutable and complementary products"
    - "privacy in sensor driven human data collection a guide for practitioners"

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)