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: "local reconstructors and tolerant testers for connectivity and diameter"
  Abstract: A local property reconstructor for a graph property is an algorithm which, given oracle access to the adjacency list of a graph that is "close" to having the property, provides oracle access to the adjacency matrix of a "correction" of the graph, i.e. a graph which has the property and is close to the given graph. For this model, we achieve local property reconstructors for the properties of connectivity and $k$-connectivity in undirected graphs, and the property of strong connectivity in directed graphs. Along the way, we present a method of transforming a local reconstructor (which acts as a "adjacency matrix oracle" for the corrected graph) into an "adjacency list oracle". This allows us to recursively use our local reconstructor for $(k-1)$-connectivity to obtain a local reconstructor for $k$-connectivity. #R##N#We also extend this notion of local property reconstruction to parametrized graph properties (for instance, having diameter at most $D$ for some parameter $D$) and require that the corrected graph has the property with parameter close to the original. We obtain a local reconstructor for the low diameter property, where if the original graph is close to having diameter $D$, then the corrected graph has diameter roughly 2D. #R##N#We also exploit a connection between local property reconstruction and property testing, observed by Brakerski, to obtain new tolerant property testers for all of the aforementioned properties. Except for the one for connectivity, these are the first tolerant property testers for these properties.

Connected through hyperedge HE-1 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems"):
  with members:
    - "an automatic speedup theorem for distributed problems"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "how much does randomness help with locally checkable problems"
    - "locality of not so weak coloring"
    - "almost global problems in the local model"
    - "the distributed complexity of locally checkable problems on paths is decidable"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "hardness of minimal symmetry breaking in distributed computing"

Connected through hyperedge HE-2 (induced by source paper "approximating the distance to monotonicity of boolean functions"):
  with members:
    - "testing unateness nearly optimally"
    - "junta correlation is testable"
    - "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "optimal unateness testers for real valued functions adaptivity helps"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-3 (induced by source paper "sampling correctors"):
  with members:
    - "sample optimal density estimation in nearly linear time"
    - "testing probability distributions underlying aggregated data"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "efficient density estimation via piecewise polynomial approximation"
    - "learning mixtures of structured distributions over discrete domains"
    - "on the power of conditional samples in distribution testing"
    - "learning poisson binomial distributions"
    - "learning k modal distributions via testing"

Connected through hyperedge HE-4 (induced by source paper "faster and simpler distributed algorithms for testing and correcting graph properties in the congest model"):
  with members:
    - "fast distributed algorithms for testing graph properties"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-5 (induced by source paper "tolerant junta testing and the connection to submodular optimization and function isomorphism"):
  with members:
    - "lower bounds for tolerant junta and unateness testing via rejection sampling of graphs"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "tolerant testers of image properties"
    - "learning with submodular functions a convex optimization perspective"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-6 (induced by source paper "local computation algorithms for graphs of non constant degrees"):
  with members:
    - "flow based algorithms for local graph clustering"
    - "distributed maximum matching in bounded degree graphs"
    - "new techniques and tighter bounds for local computation algorithms"
    - "best of two local models local centralized and local distributed algorithms"
    - "local algorithms for sparse spanning graphs"
    - "a local computation approximation scheme to maximum matching"
    - "a simple online competitive adaptation of lempel ziv compression with efficient random access support"
    - "local computation mechanism design"

Connected through hyperedge HE-7 (induced by source paper "non local probes do not help with graph problems"):
  with members:
    - "distributed maximum matching in bounded degree graphs"
    - "new techniques and tighter bounds for local computation algorithms"
    - "best of two local models local centralized and local distributed algorithms"
    - "local algorithms for sparse spanning graphs"
    - "large cuts with local algorithms on triangle free graphs"
    - "a local computation approximation scheme to maximum matching"
    - "converting online algorithms to local computation algorithms"
    - "fast local computation algorithms"

Connected through hyperedge HE-8 (induced by source paper "tolerant testers of image properties"):
  with members:
    - "tight approximation of image matching"

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)