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 positive and negative links with noisy queries theory practice"
  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 edge sign prediction problem, that 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 [Leskovec 2010]. #R##N#We model the edge sign prediction problem as follows: we are allowed to query any 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 $\delta=1-2q$ be the bias. We provide an algorithm that recovers all signs correctly with high probability in the presence of noise for any constant gap $\delta$ with $O(\frac{n\log n}{\delta^4})$ queries. Our algorithm uses breadth first search as its main algorithmic primitive. A byproduct of our proposed learning algorithm is the use of $s-t$ paths as an informative feature to predict the sign of the edge $(s,t)$. As a heuristic, we use edge disjoint $s-t$ paths of short length as a feature for predicting edge signs in real-world signed networks. Our findings suggest that the use of paths improves the classification accuracy, especially for pairs of nodes with no common neighbors.

Connected through hyperedge HE-1 (induced by source paper "correlation clustering with adaptive similarity queries"):
  with members:
    - "correlation clustering with same cluster queries bounded by optimal cost"
    - "approximate correlation clustering using same cluster queries"
    - "community recovery in graphs with locality"
    - "a survey of signed network mining in social media"
    - "on the complexity of learning with kernels"
    - "local correlation clustering"
    - "community detection thresholds and the weak ramanujan property"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "same cluster querying for overlapping clusters"):
  with members:
    - "community recovery in hypergraphs"
    - "a theoretical analysis of first heuristics of crowdsourced entity resolution"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "leveraging transitive relations for crowdsourced joins"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "optimal learning of joint alignments with a faulty oracle"):
  with members:
    - "joint alignment from pairwise differences with a noisy oracle"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "near optimal joint object matching via convex relaxation"
    - "streaming graph partitioning in the planted partition model"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-4 (induced by source paper "twittermancer predicting interactions on twitter accurately"):
  with members:
    - "twawler a lightweight twitter crawler"
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "parity queries for binary classification"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "community recovery in hypergraphs"
    - "fundamental limits on data acquisition trade offs between sample complexity and query difficulty"
    - "the set of solutions of random xorsat formulae"
    - "information recovery from pairwise measurements"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "boolean compressed sensing and noisy group testing"
    - "group testing with probabilistic tests theory design and application"

Connected through hyperedge HE-6 (induced by source paper "joint alignment from pairwise differences with a noisy oracle"):
  with members:
    - "optimal learning of joint alignments with a faulty oracle"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "clustering via crowdsourcing"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "mathematical and algorithmic analysis of network and biological data"
    - "exact recovery in the stochastic block model"
    - "a tail bound for read k families of functions"

Connected through hyperedge HE-7 (induced by source paper "hs 2 active learning over hypergraphs"):
  with members:
    - "on the minimax misclassification ratio of hypergraph community detection"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "quadratic decomposable submodular function minimization"
    - "inhomogoenous hypergraph clustering with applications"
    - "higher order organization of complex networks"
    - "clustering with same cluster queries"
    - "active learning on trees and graphs"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-8 (induced by source paper "risk averse matchings over uncertain graph databases"):
  with members:
    - "uncertain graph sparsification"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "subgraph pattern matching over uncertain graphs with identity linkage uncertainty"
    - "injecting uncertainty in graphs for identity obfuscation"
    - "capacitated team formation problem on social networks"
    - "multi skill collaborative teams based on densest subgraphs"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

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)