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: "clustering via crowdsourcing"
  Abstract: In recent years, crowdsourcing, aka human aided computation has emerged as an effective platform for solving problems that are considered complex for machines alone. Using human is time-consuming and costly due to monetary compensations. Therefore, a crowd based algorithm must judiciously use any information computed through an automated process, and ask minimum number of questions to the crowd adaptively. #R##N#One such problem which has received significant attention is {\em entity resolution}. Formally, we are given a graph $G=(V,E)$ with unknown edge set $E$ where $G$ is a union of $k$ (again unknown, but typically large $O(n^\alpha)$, for $\alpha>0$) disjoint cliques $G_i(V_i, E_i)$, $i =1, \dots, k$. The goal is to retrieve the sets $V_i$s by making minimum number of pair-wise queries $V \times V\to\{\pm1\}$ to an oracle (the crowd). When the answer to each query is correct, e.g. via resampling, then this reduces to finding connected components in a graph. On the other hand, when crowd answers may be incorrect, it corresponds to clustering over minimum number of noisy inputs. Even, with perfect answers, a simple lower and upper bound of $\Theta(nk)$ on query complexity can be shown. A major contribution of this paper is to reduce the query complexity to linear or even sublinear in $n$ when mild side information is provided by a machine, and even in presence of crowd errors which are not correctable via resampling. We develop new information theoretic lower bounds on the query complexity of clustering with side information and errors, and our upper bounds closely match with them. Our algorithms are naturally parallelizable, and also give near-optimal bounds on the number of adaptive rounds required to match the query complexity.

Connected through hyperedge HE-1 (induced by source paper "distributed clustering in the anonymized space with local differential privacy"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "comparing population means under local differential privacy with significance and power"
    - "collecting telemetry data privately"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-2 (induced by source paper "crowd powered data mining"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "multi label annotation aggregation in crowdsourcing"
    - "clamshell speeding up crowds for low latency data labeling"
    - "estimating the impact of unknown unknowns on aggregate query results"
    - "comprehensive and reliable crowd assessment algorithms"
    - "finish them pricing algorithms for human computation"
    - "leveraging transitive relations for crowdsourced joins"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-3 (induced by source paper "joint alignment from pairwise differences with a noisy oracle"):
  with members:
    - "optimal learning of joint alignments with a faulty oracle"
    - "predicting positive and negative links with noisy queries theory practice"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "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-4 (induced by source paper "predicting positive and negative links with noisy queries theory practice"):
  with members:
    - "scalable motif aware graph clustering"
    - "predicting signed edges with o n 1 o 1 log n queries"
    - "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-5 (induced by source paper "a theoretical analysis of first heuristics of crowdsourced entity resolution"):
  with members:
    - "leveraging transitive relations for crowdsourced joins"
    - "crowder crowdsourcing entity resolution"

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)