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: "four degrees of separation"
  Abstract: Frigyes Karinthy, in his 1929 short story "L\'aancszemek" ("Chains") suggested that any two persons are distanced by at most six friendship links. (The exact wording of the story is slightly ambiguous: "He bet us that, using no more than five individuals, one of whom is a personal acquaintance, he could contact the selected individual [...]". It is not completely clear whether the selected individual is part of the five, so this could actually allude to distance five or six in the language of graph theory, but the "six degrees of separation" phrase stuck after John Guare's 1990 eponymous play. Following Milgram's definition and Guare's interpretation, we will assume that "degrees of separation" is the same as "distance minus one", where "distance" is the usual path length-the number of arcs in the path.) Stanley Milgram in his famous experiment challenged people to route postcards to a fixed recipient by passing them only through direct acquaintances. The average number of intermediaries on the path of the postcards lay between 4.4 and 5.7, depending on the sample of people chosen. #R##N#We report the results of the first world-scale social-network graph-distance computations, using the entire Facebook network of active users (\approx721 million users, \approx69 billion friendship links). The average distance we observe is 4.74, corresponding to 3.74 intermediaries or "degrees of separation", showing that the world is even smaller than we expected, and prompting the title of this paper. More generally, we study the distance distribution of Facebook and of some interesting geographic subgraphs, looking also at their evolution over time. #R##N#The networks we are able to explore are almost two orders of magnitude larger than those analysed in the previous literature. We report detailed statistical metadata showing that our measurements (which rely on probabilistic algorithms) are very accurate.

Connected through hyperedge HE-1 (induced by source paper "diffusion improves graph learning"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "node embedding with adaptive similarities for scalable learning over graphs"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-2 (induced by source paper "netsmf large scale network embedding as sparse matrix factorization"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "representation learning on graphs methods and applications"
    - "network efficient distributed word2vec training system for large vocabularies"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "distance generalized core decomposition"):
  with members:
    - "mining maximal span cores from temporal networks"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "density friendly graph decomposition"
    - "the minimum wiener connector problem"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "social resilience in online communities the autopsy of friendster"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-4 (induced by source paper "computational methods in professional communication"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a nutritional label for rankings"
    - "intertemporal connections between query suggestions and search engine results for politics related queries"
    - "constructing bibliometric networks a comparison between full and fractional counting"
    - "biased assimilation homophily and the dynamics of polarization"
    - "indicators of the interdisciplinarity of journals diversity centrality and citations"

Connected through hyperedge HE-5 (induced by source paper "what makes social search efficient"):
  with members:
    - "probing limits of information spread with sequential seeding"
    - "using location based social networks to validate human mobility and relationships models"

Connected through hyperedge HE-6 (induced by source paper "the anatomy of reddit an overview of academic research"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "the aftermath of disbanding an online hateful community"
    - "tracing community genealogy how new communities emerge from the old"
    - "community interaction and conflict on the web"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "on the origins of memes by means of fringe web communities"
    - "conversation modeling on reddit using a graph structured lstm"
    - "identifying the social signals that drive online discussions a case study of reddit communities"

Connected through hyperedge HE-7 (induced by source paper "uncovering complex overlapping pattern of communities in large scale social networks"):
  with members:
    - "community detection algorithm evaluation with ground truth data"
    - "the ground truth about metadata and community detection in networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "identification of core periphery structure in networks"
    - "efficient detection of communities with significant overlaps in networks partial community merger algorithm"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "graph spectra and the detectability of community structure in networks"
    - "core periphery structure in networks"

Connected through hyperedge HE-8 (induced by source paper "learning quadratic games on networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "targeting interventions in networks"
    - "learning sparse potential games in polynomial time and sample complexity"
    - "active sensing of social networks"
    - "learning laplacian matrix in smooth graph signal representations"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"

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)