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: "the computer science and physics of community detection landscapes phase transitions and hardness"
  Abstract: Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but it is undergoing a resurgence of interest due to the need to analyze social and biological networks. While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a "ground truth" community structure built into the graph somehow. The task is then to recover the ground truth knowing only the graph. #R##N#Recently it was discovered, first heuristically in physics and then rigorously in probability and computer science, that this problem has a phase transition at which it suddenly becomes impossible. Namely, if the graph is too sparse, or the probabilistic process that generates it is too noisy, then no algorithm can find a partition that is correlated with the planted one---or even tell if there are communities, i.e., distinguish the graph from a purely random one with high probability. Above this information-theoretic threshold, there is a second threshold beyond which polynomial-time algorithms are known to succeed; in between, there is a regime in which community detection is possible, but conjectured to require exponential time. #R##N#For computer scientists, this field offers a wealth of new ideas and open questions, with connections to probability and combinatorics, message-passing algorithms, and random matrix theory. Perhaps more importantly, it provides a window into the cultures of statistical physics and statistical inference, and how those cultures think about distributions of instances, landscapes of solutions, and hardness.

Connected through hyperedge HE-1 (induced by source paper "mean field theory of graph neural networks in graph partitioning"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "algorithmic detectability threshold of the stochastic block model"
    - "deriving neural architectures from sequence and graph kernels"
    - "neural message passing for quantum chemistry"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "stochastic block models are a discrete surface tension"):
  with members:
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "efficient method for estimating the number of communities in a network"
    - "block models and personalized pagerank"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"

Connected through hyperedge HE-3 (induced by source paper "democratic summary of public opinions in free response surveys"):
  with members:
    - "network structure from rich but noisy data"
    - "comparative analysis on the selection of number of clusters in community detection"
    - "reconstructing networks with unknown and heterogeneous errors"
    - "algorithmic detectability threshold of the stochastic block model"
    - "algorithmic infeasibility of community detection in higher order networks"
    - "cross validation estimate of the number of clusters in a network"
    - "estimating the number of communities in a network"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-4 (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"
    - "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 semisupervised clustering of sparse networks"
    - "phase transitions in community detection a solvable toy model"

Connected through hyperedge HE-5 (induced by source paper "information theoretic and algorithmic thresholds for group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "group testing algorithms bounds and simulations"

Connected through hyperedge HE-6 (induced by source paper "charting the replica symmetric phase"):
  with members:
    - "information theoretic thresholds from the cavity method"
    - "planting colourings silently"
    - "the condensation phase transition in random graph coloring"
    - "the phase transition in random regular exact cover"
    - "on the complexity of random satisfiability problems with planted solutions"
    - "community detection thresholds and the weak ramanujan property"
    - "on the chromatic number of a random hypergraph"
    - "reconstruction and clustering in random constraint satisfaction problems"

Connected through hyperedge HE-7 (induced by source paper "partial recovery of erd h o s r e nyi graph alignment via k core alignment"):
  with members:
    - "fundamental limits of database alignment"
    - "nearly efficient algorithms for the graph matching problem on correlated random graphs"
    - "seeded graph matching via large neighborhood statistics"
    - "typicality matching for pairs of correlated graphs"
    - "blind de anonymization attacks using social networks"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "spectral alignment of networks"

Connected through hyperedge HE-8 (induced by source paper "the lov asz theta function for random regular graphs and community detection in the hard regime"):
  with members:
    - "charting the replica symmetric phase"
    - "information theoretic thresholds from the cavity method"
    - "sum of squares lower bounds for refuting any csp"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "how to refute a random csp"
    - "exact recovery in the stochastic block model"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "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.CC(Computational Complexity)