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: "information theoretic thresholds from the cavity method"
  Abstract: Abstract   Vindicating a sophisticated but non-rigorous physics approach called the cavity method, we establish a formula for the mutual information in statistical inference problems induced by random graphs and we show that the mutual information holds the key to understanding certain important phase transitions in random graph models. We work out several concrete applications of these general results. For instance, we pinpoint the exact condensation phase transition in the Potts antiferromagnet on the random graph, thereby improving prior approximate results (Contucci et al., 2013)  [34] . Further, we prove the conjecture from Krzakala et al. (2007)  [55]  about the condensation phase transition in the random graph coloring problem for any number    q  ≥  3    of colors. Moreover, we prove the conjecture on the information-theoretic threshold in the disassortative stochastic block model (Decelle et al., 2011)  [35] . Additionally, our general result implies the conjectured formula for the mutual information in Low-Density Generator Matrix codes (Montanari, 2005)  [73] .

Connected through hyperedge HE-1 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models"):
  with members:
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "opening the black box of deep neural networks via information"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-2 (induced by source paper "overlap matrix concentration in optimal bayesian inference"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "rank one matrix estimation analysis of algorithmic and information theoretic limits by the spatial coupling method"
    - "the mutual information in random linear estimation beyond i i d matrices"
    - "adaptive path interpolation for sparse systems application to a simple censored block model"

Connected through hyperedge HE-3 (induced by source paper "concentration of the matrix valued minimum mean square error in optimal bayesian inference"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "rank one matrix estimation analysis of algorithmic and information theoretic limits by the spatial coupling method"
    - "the mutual information in random linear estimation beyond i i d matrices"

Connected through hyperedge HE-4 (induced by source paper "quantitative group testing in the sublinear regime"):
  with members:
    - "sparse graph codes for non adaptive quantitative group testing"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "the capacity of bernoulli nonadaptive group testing"
    - "decoding from pooled data phase transitions of message passing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"

Connected through hyperedge HE-5 (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"
    - "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 semisupervised clustering of sparse networks"
    - "phase transitions in community detection a solvable toy model"

Connected through hyperedge HE-6 (induced by source paper "mutual information for the stochastic block model by the adaptive interpolation method"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "adaptive path interpolation for sparse systems application to a simple censored block model"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-7 (induced by source paper "adaptive path interpolation for sparse systems application to a simple censored block model"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "entropy and mutual information in models of deep neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"

Connected through hyperedge HE-8 (induced by source paper "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "bayesian estimation from few samples community detection and related problems"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "information theoretically optimal sparse pca"
    - "a statistical model for tensor pca"
    - "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.DM(Discrete Mathematics)