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: "predicting signed edges with o n 1 o 1 log n queries"
  Abstract: Social networks and interactions in social media involve both positive and negative relationships. Signed graphs capture both types of relationships: positive edges correspond to pairs of "friends", and negative edges to pairs of "foes". The {\em edge sign prediction problem}, which aims to predict whether an interaction between a pair of nodes will be positive or negative, is an important graph mining task for which many heuristics have recently been proposed \cite{leskovec2010predicting,leskovec2010signed}. #R##N#Motivated by social balance theory, we model the edge sign prediction problem as a noisy correlation clustering problem with two clusters. We are allowed to query each pair of nodes whether they belong to the same cluster or not, but the answer to the query is corrupted with some probability $0<q<\frac{1}{2}$. Let $c=\frac{1}{2}-q$ be the gap. We provide an algorithm that recovers the clustering with high probability in the presence of noise for any constant gap $c$ with $O(n^{1+\tfrac{1}{\log\log{n}}}\log{n})$ queries. Our algorithm uses simple breadth first search as its main algorithmic primitive. Finally, we provide a novel generalization to $k \geq 3$ clusters and prove that our techniques can recover the clustering if the gap is constant in this generalized setting.

Connected through hyperedge HE-1 (induced by source paper "predicting positive and negative links with noisy queries theory practice"):
  with members:
    - "scalable motif aware graph clustering"
    - "clustering via crowdsourcing"
    - "a new correlation clustering method for cancer mutation analysis"
    - "correlation clustering with noisy partial information"
    - "streaming graph partitioning in the planted partition model"
    - "analysis of boolean functions"
    - "mathematical and algorithmic analysis of network and biological data"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-2 (induced by source paper "approximate correlation clustering using same cluster queries"):
  with members:
    - "approximate clustering with same cluster queries"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "clustering with same cluster queries"
    - "efficient clustering with limited distance information"
    - "correlation clustering with noisy partial information"
    - "local algorithms for interactive clustering"
    - "clustering under perturbation resilience"
    - "correlation clustering with a fixed number of clusters"

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)