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: "phase transitions in semisupervised clustering of sparse networks"
  Abstract: Predicting labels of nodes in a network, such as community memberships or demographic variables, is an important problem with applications in social and biological networks. A recently-discovered phase transition puts fundamental limits on the accuracy of these predictions if we have access only to the network topology. However, if we know the correct labels of some fraction $\alpha$ of the nodes, we can do better. We study the phase diagram of this "semisupervised" learning problem for networks generated by the stochastic block model. We use the cavity method and the associated belief propagation algorithm to study what accuracy can be achieved as a function of $\alpha$. For $k = 2$ groups, we find that the detectability transition disappears for any $\alpha > 0$, in agreement with previous work. For larger $k$ where a hard but detectable regime exists, we find that the easy/hard transition (the point at which efficient algorithms can do better than chance) becomes a line of transitions where the accuracy jumps discontinuously at a critical value of $\alpha$. This line ends in a critical point with a second-order transition, beyond which the accuracy is a continuous function of $\alpha$. We demonstrate qualitatively similar transitions in two real-world networks.

Connected through hyperedge HE-1 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff"):
  with members:
    - "graph powering and spectral robustness"
    - "a survey on theoretical advances of community detection in networks"
    - "connectivity in random annulus graphs and the geometric block model"
    - "active community detection a maximum likelihood approach"
    - "active learning for community detection in stochastic block models"
    - "s2 an efficient graph based active learning algorithm with application to nonparametric classification"
    - "exact recovery in the stochastic block model"
    - "active learning on trees and graphs"

Connected through hyperedge HE-2 (induced by source paper "mutual information in community detection with covariate information and correlated networks"):
  with members:
    - "the geometry of community detection via the mmse matrix"
    - "community detection with side information exact recovery under the stochastic block model"
    - "information theoretic thresholds from the cavity method"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "structure and inference in annotated networks"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in community detection a solvable toy model"

Connected through hyperedge HE-3 (induced by source paper "classifying partially labeled networked data via logistic network lasso"):
  with members:
    - "network lasso clustering and optimization in large graphs"
    - "signal recovery on graphs variation minimization"

Connected through hyperedge HE-4 (induced by source paper "l gamma pagerank for semi supervised learning"):
  with members:
    - "mean field analysis of personalized pagerank with implications for local graph clustering"
    - "generalized optimization framework for graph based semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "algorithmic detectability threshold of the stochastic block model"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "structure and inference in annotated networks"
    - "estimating the number of communities in a network"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-6 (induced by source paper "mean field analysis of personalized pagerank with implications for local graph clustering"):
  with members:
    - "block models and personalized pagerank"
    - "the power of side information in subgraph detection"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "random walks and evolving sets faster convergences and limitations"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "distribution of pagerank mass among principle components of the web"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-7 (induced by source paper "contextual stochastic block models"):
  with members:
    - "algorithmic decision making and the cost of fairness"
    - "clustering attributed graphs models measures and methods"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "partitioning networks with node attributes by compressing information flow"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "latent multi group membership graph model"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-8 (induced by source paper "the computer science and physics of community detection landscapes phase transitions and hardness"):
  with members:
    - "information theoretic thresholds from the cavity method"
    - "spectral redemption in clustering sparse 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)