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: "fast bidirectional probability estimation in markov models"
  Abstract: We develop a new bidirectional algorithm for estimating Markov chain multi-step transition probabilities: given a Markov chain, we want to estimate the probability of hitting a given target state in $\ell$ steps after starting from a given source distribution. Given the target state $t$, we use a (reverse) local power iteration to construct an `expanded target distribution', which has the same mean as the quantity we want to estimate, but a smaller variance -- this can then be sampled efficiently by a Monte Carlo algorithm. Our method extends to any Markov chain on a discrete (finite or countable) state-space, and can be extended to compute functions of multi-step transition probabilities such as PageRank, graph diffusions, hitting/return times, etc. Our main result is that in `sparse' Markov Chains -- wherein the number of transitions between states is comparable to the number of states -- the running time of our algorithm for a uniform-random target node is order-wise smaller than Monte Carlo and power iteration based algorithms; in particular, our method can estimate a probability $p$ using only $O(1/\sqrt{p})$ running time.

Connected through hyperedge HE-1 (induced by source paper "empirical policy evaluation with supergraphs"):
  with members:
    - "forward backward reinforcement learning"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"
    - "on the role of clustering in personalized pagerank estimation"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "personalized pagerank to a target node"

Connected through hyperedge HE-2 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "parallel local graph clustering"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"

Connected through hyperedge HE-3 (induced by source paper "on approximating the stationary distribution of time reversible markov chains"):
  with members:
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "solving systems of linear equations locally and asynchronously"
    - "sublinear algorithms for local graph centrality estimation"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-4 (induced by source paper "personalized pagerank estimation and search a bidirectional approach"):
  with members:
    - "efficient algorithms for personalized pagerank"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "personalized pagerank to a target node"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-5 (induced by source paper "efficient algorithms for personalized pagerank"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a note on the pagerank of undirected graphs"
    - "fast distributed pagerank computation"
    - "learning fast mixing models for structured prediction"
    - "bidirectional pagerank estimation from average case to worst case"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-6 (induced by source paper "bidirectional pagerank estimation from average case to worst case"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a note on the pagerank of undirected graphs"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "personalized pagerank with node dependent restart"
    - "pagerank beyond the web"
    - "sublinear algorithms for local graph centrality estimation"
    - "approximating pagerank locally with sublinear query complexity"

Connected through hyperedge HE-7 (induced by source paper "sublinear algorithms for local graph centrality estimation"):
  with members:
    - "on approximating the stationary distribution of time reversible markov chains"
    - "an optimization approach to locally biased graph algorithms"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "the power of local information in pagerank"
    - "simple set cardinality estimation through random sampling"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "bidirectional pagerank estimation from average case to worst case"
    - "heat kernel based 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.DS(Data Structures and Algorithms)