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: "snap a general purpose network analysis and graph mining library"
  Abstract: Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. #R##N#Here, we describe Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy to use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs where nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and meta-data on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic, they can be modified during the computation at low cost. SNAP is provided as an open source library in C++ as well as a module in Python. #R##N#We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.

Connected through hyperedge HE-1 (induced by source paper "adaptive low level storage of very large knowledge graphs"):
  with members:
    - "a survey of rdf data management systems"
    - "kognac efficient encoding of large knowledge graphs"
    - "taming subgraph isomorphism for rdf query processing"
    - "ringo interactive graph analytics on big memory machines"
    - "s2rdf rdf querying with sparql on spark"
    - "processing sparql queries over distributed rdf graphs"

Connected through hyperedge HE-2 (induced by source paper "struct mmsb mixed membership stochastic blockmodels with interpretable structured priors"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "discriminative nonparametric latent feature relational models with data augmentation"
    - "spectral networks and locally connected networks on graphs"
    - "community detection in networks with node attributes"
    - "the nonparametric metadata dependent relational model"
    - "latent multi group membership graph model"

Connected through hyperedge HE-3 (induced by source paper "faster fully dynamic transitive closure in practice"):
  with members:
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "fully dynamic single source reachability in practice an experimental study"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "mantaining dynamic matrices for fully dynamic transitive closure"

Connected through hyperedge HE-4 (induced by source paper "superbubbles as an empirical characteristic of directed networks"):
  with members:
    - "detecting superbubbles in assembly graphs"

Connected through hyperedge HE-5 (induced by source paper "big data analytics for large scale wireless networks challenges and opportunities"):
  with members:
    - "blockchain for internet of things a survey"
    - "big data analytics for manufacturing internet of things opportunities challenges and enabling technologies"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "stealing hyperparameters in machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"

Connected through hyperedge HE-6 (induced by source paper "initialization for network embedding a graph partition approach"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mile a multi level framework for scalable graph embedding"
    - "a tutorial on network embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"

Connected through hyperedge HE-7 (induced by source paper "optimizing generalized pagerank methods for seed expansion community detection"):
  with members:
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "quadratic decomposable submodular function minimization theory and practice"
    - "diffusion improves graph learning"
    - "mean field analysis of personalized pagerank with implications for local graph clustering"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "block models and personalized pagerank"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "benchmarks for graph embedding evaluation"):
  with members:
    - "link prediction based on graph neural networks"
    - "graph embedding techniques applications and performance a survey"
    - "inductive representation learning on large graphs"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for 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)