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: "influence maximization in continuous time diffusion networks"
  Abstract: The problem of finding the optimal set of source nodes in a diffusion network that maximizes the spread of information, influence, and diseases in a limited amount of time depends dramatically on the underlying temporal dynamics of the network. However, this still remains largely unexplored to date. To this end, given a network and its temporal dynamics, we first describe how continuous time Markov chains allow us to analytically compute the average total number of nodes reached by a diffusion process starting in a set of source nodes. We then show that selecting the set of most influential source nodes in the continuous time influence maximization problem is NP-hard and develop an efficient approximation algorithm with provable near-optimal performance. Experiments on synthetic and real diffusion networks show that our algorithm outperforms other state of the art algorithms by at least ~20% and is robust across different network topologies.

Connected through hyperedge HE-1 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"

Connected through hyperedge HE-2 (induced by source paper "self activation influence maximization"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial classification on social networks"):
  with members:
    - "a general retraining framework for scalable adversarial classification"
    - "antisocial behavior in online discussion communities"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-4 (induced by source paper "robust budget allocation via continuous submodular functions"):
  with members:
    - "robust monotone submodular function maximization"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "robust influence maximization"
    - "robust influence maximization"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"

Connected through hyperedge HE-5 (induced by source paper "influence maximization over markovian graphs a stochastic optimization approach"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "scalable methods for adaptively seeding a social network"
    - "exact recovery in the stochastic block model"
    - "interactive sensing and decision making in social networks"
    - "uncovering the temporal dynamics of diffusion networks"
    - "information spreading in stationary markovian evolving graphs"

Connected through hyperedge HE-6 (induced by source paper "popular topics spread faster new dimension for influence propagation in online social networks"):
  with members:
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "minimizing seed set selection with probabilistic coverage guarantee in a social network"
    - "sketch based influence maximization and computation scaling up with guarantees"

Connected through hyperedge HE-7 (induced by source paper "adaptive influence maximization in dynamic social networks"):
  with members:
    - "scalable methods for adaptively seeding a social network"
    - "minimizing seed set selection with probabilistic coverage guarantee in a social network"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "controlling elections through social influence"):
  with members:
    - "robust influence maximization"
    - "elections with few voters candidate control can be easy"
    - "sketch based influence maximization and computation scaling up with guarantees"

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)