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: "estimating subgraph frequencies with or without attributes from egocentrically sampled data"
  Abstract: In this paper we show how to efficiently produce unbiased estimates of subgraph frequencies from a probability sample of egocentric networks (i.e., focal nodes, their neighbors, and the induced subgraphs of ties among their neighbors). A key feature of our proposed method that differentiates it from prior methods is the use of egocentric data. Because of this, our method is suitable for estimation in large unknown graphs, is easily parallelizable, handles privacy sensitive network data (e.g. egonets with no neighbor labels), and supports counting of large subgraphs (e.g. maximal clique of size 205 in Section 6) by building on top of existing exact subgraph counting algorithms that may not support sampling. It gracefully handles a variety of sampling designs such as uniform or weighted independence or random walk sampling. Our method can be used for subgraphs that are: (i) undirected or directed; (ii) induced or non-induced; (iii) maximal or non-maximal; and (iv) potentially annotated with attributes. We compare our estimators on a variety of real-world graphs and sampling methods and provide suggestions for their use. Simulation shows that our method outperforms the state-of-the-art approach for relative subgraph frequencies by up to an order of magnitude for the same sample size. Finally, we apply our methodology to a rare sample of Facebook users across the social graph to estimate and interpret the clique size distribution and gender composition of cliques.

Connected through hyperedge HE-1 (induced by source paper "a general framework for estimating graphlet statistics via random walk"):
  with members:
    - "leveraging history for faster sampling of online social networks"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets"
    - "minfer inferring motif statistics from sampled edges"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "graph based anomaly detection and description a survey"
    - "efficiently estimating motif statistics of large 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)