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: "inferring networks of diffusion and influence"
  Abstract: Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. #R##N#We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "iad interaction aware diffusion framework in social networks"
    - "deepinf social influence prediction with deep learning"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"

Connected through hyperedge HE-2 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"):
  with members:
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "deepinf social influence prediction with deep learning"
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "motif based convolutional neural network on graphs"
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "learning dynamic and personalized comorbidity networks from event data using deep diffusion processes"):
  with members:
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-4 (induced by source paper "inferring individual level causal models from graph based relational time series"):
  with members:
    - "designing and deploying online field experiments"
    - "graph cluster randomization network exposure to multiple universes"
    - "a dynamical system for pagerank with time dependent teleportation"

Connected through hyperedge HE-5 (induced by source paper "distributed submodular maximization with parallel execution"):
  with members:
    - "the impact of information in distributed submodular maximization"
    - "a parallel double greedy algorithm for submodular maximization"
    - "distributed submodular maximization with limited information"

Connected through hyperedge HE-6 (induced by source paper "online non monotone dr submodular maximization"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "potential function proofs for first order methods"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

Connected through hyperedge HE-7 (induced by source paper "parsimonious black box adversarial attacks via efficient combinatorial optimization"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"
    - "wide residual networks"

Connected through hyperedge HE-8 (induced by source paper "non monotone dr submodular maximization approximation and regret guarantees"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "non monotone dr submodular function maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

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.DS(Data Structures and Algorithms)