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 scalable heuristic for viral marketing under the tipping model"
  Abstract: In a "tipping" model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial "seed" set from the network such that the entire network adopts any behavior given to the seed. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases. In addition, our approach scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed set in under 3.6 hours. Our experiments also indicate that our algorithm provides small seed sets even if high-degree nodes are removed. Lastly, we find that highly clustered local neighborhoods, together with dense network-wide community structures, suppress a trend's ability to spread under the tipping model.

Connected through hyperedge HE-1 (induced by source paper "influence maximization for fixed heterogeneous thresholds"):
  with members:
    - "sequential seeding in complex networks trading speed for coverage"
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-2 (induced by source paper "latent viral marketing concepts and control methods"):
  with members:
    - "information is not a virus and other consequences of human cognitive limits"
    - "sequential seeding in complex networks trading speed for coverage"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-3 (induced by source paper "discovering small target sets in social networks a fast and effective algorithm"):
  with members:
    - "on finding small sets that influence large networks"
    - "spread of influence in weighted networks under time and budget constraints"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "influence diffusion in social networks under time window constraints"
    - "contagious sets in expanders"
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-4 (induced by source paper "on finding small sets that influence large networks"):
  with members:
    - "spread of influence in weighted networks under time and budget constraints"
    - "contagious sets in dense graphs"
    - "parameterized approximability of maximizing the spread of influence in networks"
    - "latency bounded target set selection in social networks"
    - "influence diffusion in social networks under time window constraints"
    - "contagious sets in expanders"
    - "target set selection problem for honeycomb networks"

Connected through hyperedge HE-5 (induced by source paper "online social network analysis a survey of research applications in computer science"):
  with members:
    - "inheritance patterns in citation networks reveal scientific memes"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "can cascades be predicted"
    - "exploring image virality in google plus"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "a meta analysis of state of the art electoral prediction from twitter data"

Connected through hyperedge HE-6 (induced by source paper "understanding image virality"):
  with members:
    - "becoming the expert interactive multi class machine teaching"
    - "cloudcv large scale distributed computer vision as a cloud service"
    - "deep feelings a massive cross lingual study on the relation between emotions and virality"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "exploring image virality in google plus"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)