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: "exact recovery in the stochastic block model"
  Abstract: The stochastic block model (SBM) with two communities, or equivalently the planted bisection model, is a popular model of random graph exhibiting a cluster behaviour. In the symmetric case, the graph has two equally sized clusters and vertices connect with probability $p$ within clusters and $q$ across clusters. In the past two decades, a large body of literature in statistics and computer science has focused on providing lower-bounds on the scaling of $|p-q|$ to ensure exact recovery. In this paper, we identify a sharp threshold phenomenon for exact recovery: if $\alpha=pn/\log(n)$ and $\beta=qn/\log(n)$ are constant (with $\alpha>\beta$), recovering the communities with high probability is possible if $\frac{\alpha+\beta}{2} - \sqrt{\alpha \beta}>1$ and impossible if $\frac{\alpha+\beta}{2} - \sqrt{\alpha \beta}<1$. In particular, this improves the existing bounds. This also sets a new line of sight for efficient clustering algorithms. While maximum likelihood (ML) achieves the optimal threshold (by definition), it is in the worst-case NP-hard. This paper proposes an efficient algorithm based on a semidefinite programming relaxation of ML, which is proved to succeed in recovering the communities close to the threshold, while numerical experiments suggest it may achieve the threshold. An efficient algorithm which succeeds all the way down to the threshold is also obtained using a partial recovery algorithm combined with a local improvement procedure.

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"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "active learning on trees and graphs"

Connected through hyperedge HE-2 (induced by source paper "local access to huge random objects through partial sampling"):
  with members:
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "on the probe complexity of local computation algorithms"
    - "sublinear random access generators for preferential attachment graphs"
    - "parallel algorithms for generating random networks with given degree sequences"
    - "fast local computation algorithms"

Connected through hyperedge HE-3 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "high dimensional robust sparse regression"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"

Connected through hyperedge HE-4 (induced by source paper "planted models for k way edge and vertex expansion"):
  with members:
    - "semi random graphs with planted sparse vertex cuts algorithms for exact and approximate recovery"
    - "multisection in the stochastic block model using semidefinite programming"
    - "learning communities in the presence of errors"
    - "how robust are reconstruction thresholds for community detection"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "the complexity of approximating vertex expansion"

Connected through hyperedge HE-5 (induced by source paper "statistical guarantees for local graph clustering"):
  with members:
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "parallel local graph clustering"
    - "a simple and strongly local flow based method for cut improvement"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "on semidefinite relaxations for the block model"

Connected through hyperedge HE-6 (induced by source paper "graph coarsening with preserved spectral properties"):
  with members:
    - "network density of states"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "solving graph compression via optimal transport"
    - "netlsd hearing the shape of a graph"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-7 (induced by source paper "local statistics semidefinite programming and community detection"):
  with members:
    - "a tight degree 4 sum of squares lower bound for the sherrington kirkpatrick hamiltonian"
    - "vector colorings of random ramanujan and large girth irregular graphs"
    - "computational hardness of certifying bounds on constrained pca problems"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "learning from untrusted data"
    - "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-8 (induced by source paper "step by step community detection for volume regular graphs"):
  with members:
    - "distributed community detection via metastability of the 2 choices dynamics"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "eigenvector computation and community detection in asynchronous gossip models"
    - "distributed graph clustering and sparsification"
    - "find your place simple distributed algorithms for community detection"
    - "partitioning well clustered graphs spectral clustering works"
    - "spectral redemption in clustering sparse networks"
    - "on the analysis of a label propagation algorithm for community detection"

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)