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: "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs"
  Abstract: Exploring small connected and induced subgraph patterns (CIS patterns, or graphlets) has recently attracted considerable attention. Despite recent efforts on computing the number of instances a specific graphlet appears in a large graph (i.e., the total number of CISes isomorphic to the graphlet), little attention has been paid to characterizing a node's graphlet degree, i.e., the number of CISes isomorphic to the graphlet that include the node, which is an important metric for analyzing complex networks such as social and biological networks. Similar to global graphlet counting, it is challenging to compute node graphlet degrees for a large graph due to the combinatorial nature of the problem. Unfortunately, previous methods of computing global graphlet counts are not suited to solve this problem. In this paper we propose sampling methods to estimate node graphlet degrees for undirected and directed graphs, and analyze the error of our estimates. To the best of our knowledge, we are the first to study this problem and give a fast scalable solution. We conduct experiments on a variety of real-word datasets that demonstrate that our methods accurately and efficiently estimate node graphlet degrees for graphs with millions of edges.

Connected through hyperedge HE-1 (induced by source paper "motivo fast motif counting via succinct color coding and adaptive sampling"):
  with members:
    - "subgraph counting color coding beyond trees"
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "a general framework for estimating graphlet statistics via random walk"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "efficiently estimating motif statistics of large networks"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-2 (induced by source paper "graphlets versus node2vec and struc2vec in the task of network alignment"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "predicting multicellular function through multi layer tissue networks"
    - "a survey on network embedding"
    - "alignment of dynamic networks"
    - "node2vec scalable feature learning for networks"
    - "evaluating link prediction methods"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "clustering and community detection in directed networks a survey"

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)