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: "community detection via random and adaptive sampling"
  Abstract: In this paper, we consider networks consisting of a finite number of non-overlapping communities. To extract these communities, the interaction between pairs of nodes may be sampled from a large available data set, which allows a given node pair to be sampled several times. When a node pair is sampled, the observed outcome is a binary random variable, equal to 1 if nodes interact and to 0 otherwise. The outcome is more likely to be positive if nodes belong to the same communities. For a given budget of node pair samples or observations, we wish to jointly design a sampling strategy (the sequence of sampled node pairs) and a clustering algorithm that recover the hidden communities with the highest possible accuracy. We consider both non-adaptive and adaptive sampling strategies, and for both classes of strategies, we derive fundamental performance limits satisfied by any sampling and clustering algorithm. In particular, we provide necessary conditions for the existence of algorithms recovering the communities accurately as the network size grows large. We also devise simple algorithms that accurately reconstruct the communities when this is at all possible, hence proving that the proposed necessary conditions for accurate community detection are also sufficient. The classical problem of community detection in the stochastic block model can be seen as a particular instance of the problems consider here. But our framework covers more general scenarios where the sequence of sampled node pairs can be designed in an adaptive manner. The paper provides new results for the stochastic block model, and extends the analysis to the case of adaptive sampling.

Connected through hyperedge HE-1 (induced by source paper "dyncomm r package dynamic community detection for evolving networks"):
  with members:
    - "density based community detection optimization"
    - "community discovery in dynamic networks a survey"
    - "a linear streaming algorithm for community detection in very large networks"
    - "streaming memory limited algorithms for community detection"

Connected through hyperedge HE-2 (induced by source paper "scalable and robust community detection with randomized sketching"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "efficient clustering with limited distance information"
    - "community detection thresholds and the weak ramanujan property"
    - "sparse subspace clustering algorithm theory and applications"
    - "clustering partially observed graphs via convex optimization"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "community detection with side information exact recovery under the stochastic block model"):
  with members:
    - "the power of side information in subgraph detection"
    - "structure and inference in annotated networks"
    - "exact recovery in the stochastic block model"
    - "a tensor approach to learning mixed membership community models"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in networks with node attributes"
    - "comparative study for inference of hidden classes in stochastic block models"

Connected through hyperedge HE-4 (induced by source paper "side information in the binary stochastic block model exact recovery"):
  with members:
    - "the power of side information in subgraph detection"
    - "structure and inference in annotated networks"
    - "exact recovery in the stochastic block model"
    - "a tensor approach to learning mixed membership community models"
    - "community detection thresholds and the weak ramanujan property"
    - "community detection in networks with node attributes"
    - "comparative study for inference of hidden classes in stochastic block models"

Connected through hyperedge HE-5 (induced by source paper "accurate community detection in the stochastic block model via spectral algorithms"):
  with members:
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-6 (induced by source paper "streaming memory limited algorithms for community detection"):
  with members:
    - "community detection thresholds and the weak ramanujan property"

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)