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 recovery in graphs with locality"
  Abstract: Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality. In this problem, pairwise noisy measurements of whether two nodes are in the same community or different communities come mainly or exclusively from nearby nodes rather than uniformly sampled between all nodes pairs, as in most existing models. We present an algorithm that runs nearly linearly in the number of measurements and which achieves the information theoretic limit for exact recovery.

Connected through hyperedge HE-1 (induced by source paper "correlation clustering with adaptive similarity queries"):
  with members:
    - "correlation clustering with same cluster queries bounded by optimal cost"
    - "predicting positive and negative links with noisy queries theory practice"
    - "approximate correlation clustering using same cluster queries"
    - "a survey of signed network mining in social media"
    - "on the complexity of learning with kernels"
    - "local correlation clustering"
    - "community detection thresholds and the weak ramanujan property"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "community recovery in hypergraphs"):
  with members:
    - "fast algorithms for robust pca via gradient descent"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "clustering with same cluster queries"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "spectral detection on sparse hypergraphs"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-3 (induced by source paper "the projected power method an efficient algorithm for joint alignment from pairwise differences"):
  with members:
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "f divergence inequalities"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"

Connected through hyperedge HE-4 (induced by source paper "detecting communities is hard and counting them is even harder"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "how robust are reconstruction thresholds for community detection"
    - "eth hardness for signaling in symmetric zero sum games"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "mixture selection mechanism design and signaling"
    - "am with multiple merlins"
    - "axioms for graph clustering quality functions"
    - "finding endogenously formed communities"

Connected through hyperedge HE-5 (induced by source paper "information recovery from pairwise measurements"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "bounds among f divergences"
    - "discrete signal processing on graphs sampling theory"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "near optimal joint object matching via convex relaxation"
    - "accurate community detection in the stochastic block model via spectral algorithms"

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.IT(Information Theory)