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: "adaptive simulated annealing a near optimal connection between sampling and counting"
  Abstract: We present a near-optimal reduction from approximately counting the cardinality of a discrete set to approximately sampling elements of the set. An important application of our work is to approximating the partition function $Z$ of a discrete system, such as the Ising model, matchings or colorings of a graph. The typical approach to estimating the partition function $Z(\beta^*)$ at some desired inverse temperature $\beta^*$ is to define a sequence, which we call a {\em cooling schedule}, $\beta_0=0<\beta_1<...<\beta_\ell=\beta^*$ where Z(0) is trivial to compute and the ratios $Z(\beta_{i+1})/Z(\beta_i)$ are easy to estimate by sampling from the distribution corresponding to $Z(\beta_i)$. Previous approaches required a cooling schedule of length $O^*(\ln{A})$ where $A=Z(0)$, thereby ensuring that each ratio $Z(\beta_{i+1})/Z(\beta_i)$ is bounded. We present a cooling schedule of length $\ell=O^*(\sqrt{\ln{A}})$. #R##N#For well-studied problems such as estimating the partition function of the Ising model, or approximating the number of colorings or matchings of a graph, our cooling schedule is of length $O^*(\sqrt{n})$, which implies an overall savings of $O^*(n)$ in the running time of the approximate counting algorithm (since roughly $\ell$ samples are needed to estimate each ratio).

Connected through hyperedge HE-1 (induced by source paper "fast sampling and counting k sat solutions in the local lemma regime"):
  with members:
    - "algorithms for bis hard problems on expander graphs"
    - "algorithmic pirogov sinai theory"
    - "counting hypergraph colorings in the local lemma regime"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "a faster approximation algorithm for the gibbs partition function"
    - "approximate counting the lovasz local lemma and inference in graphical models"
    - "parallel algorithms and concentration bounds for the lovasz local lemma via witness dags"
    - "approximation via correlation decay when strong spatial mixing fails"

Connected through hyperedge HE-2 (induced by source paper "swendsen wang dynamics for general graphs in the tree uniqueness region"):
  with members:
    - "spatial mixing and non local markov chains"
    - "random cluster dynamics for the ising model is rapidly mixing"
    - "spatial mixing and the connective constant optimal bounds"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-3 (induced by source paper "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"):
  with members:
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "fptas for bis with degree bounds on one side"
    - "a simple fptas for counting edge covers"
    - "spatial mixing and the connective constant optimal bounds"
    - "loopy belief propagation for approximate inference an empirical study"
    - "spatial mixing and approximation algorithms for graphs with bounded connective constant"
    - "improved bounds on the phase transition for the hard core model in 2 dimensions"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"

Connected through hyperedge HE-4 (induced by source paper "a combination of testability and decodability by tensor products"):
  with members:
    - "list decoding tensor products and interleaved codes"
    - "improved nearly mds expander codes"
    - "robust locally testable codes and products of codes"
    - "some applications of coding theory in computational complexity"

Connected through hyperedge HE-5 (induced by source paper "improved inapproximability results for counting independent sets in the hard core model"):
  with members:
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-6 (induced by source paper "a unified framework for testing linear invariant properties"):
  with members:
    - "every locally characterized affine invariant property is testable"
    - "testing low complexity affine invariant properties"
    - "partially symmetric functions are efficiently isomorphism testable"
    - "separations of matroid freeness properties"
    - "succinct representation of codes with applications to testing"

Connected through hyperedge HE-7 (induced by source paper "submodular maximization by simulated annealing"):
  with members:
    - "symmetry and approximability of submodular maximization problems"
    - "constrained non monotone submodular maximization offline and secretary algorithms"
    - "improved inapproximability for submodular maximization"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-8 (induced by source paper "learning to predict combinatorial structures"):
  with members:
    - "learning as search optimization approximate large margin methods for structured prediction"
    - "learning graph matching"
    - "direct optimization of ranking measures"

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.DS(Data Structures and Algorithms)