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: "community recovery in hypergraphs"
  Abstract: Community recovery is a central problem that arises in a wide variety of applications such as network clustering, motion segmentation, face clustering and protein complex detection. The objective of the problem is to cluster data points into distinct communities based on a set of measurements, each of which is associated with the values of a certain number of data points. While most of the prior works focus on a setting in which the number of data points involved in a measurement is two, this work explores a generalized setting in which the number can be more than two. Motivated by applications particularly in machine learning and channel coding, we consider two types of measurements: (1) homogeneity measurement which indicates whether or not the associated data points belong to the same community; (2) parity measurement which denotes the modulo-2 sum of the values of the data points. Such measurements are possibly corrupted by Bernoulli noise. We characterize the fundamental limits on the number of measurements required to reconstruct the communities for the considered models.

Connected through hyperedge HE-1 (induced by source paper "crowdsourced classification with xor queries fundamental limits and an efficient algorithm"):
  with members:
    - "iterative bayesian learning for crowdsourced regression"
    - "optimality of belief propagation for crowdsourced classification"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "imagenet large scale visual recognition challenge"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-2 (induced by source paper "active learning in the geometric block model"):
  with members:
    - "graph powering and spectral robustness"
    - "connectivity in random annulus graphs and the geometric block model"
    - "the geometric block model"
    - "active learning for community detection in stochastic block models"
    - "s2 an efficient graph based active learning algorithm with application to nonparametric classification"
    - "active learning on trees and graphs"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "same cluster querying for overlapping clusters"):
  with members:
    - "predicting positive and negative links with noisy queries theory practice"
    - "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-4 (induced by source paper "parity queries for binary classification"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "predicting positive and negative links with noisy queries theory practice"
    - "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-5 (induced by source paper "exact recovery in the hypergraph stochastic block model a spectral algorithm"):
  with members:
    - "on the minimax misclassification ratio of hypergraph community detection"
    - "recovering nonuniform planted partitions via iterated projection"
    - "spectral detection on sparse hypergraphs"
    - "a simple spectral algorithm for recovering planted partitions"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-6 (induced by source paper "on the minimax misclassification ratio of hypergraph community detection"):
  with members:
    - "motif clustering and overlapping clustering for social network analysis"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "spectral detection on sparse hypergraphs"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-7 (induced by source paper "community detection in hypergraphs spiked tensor models and sum of squares"):
  with members:
    - "certifying random polynomials over the unit sphere via sum of squares hierarchy"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "tensor principal component analysis via sum of squares proofs"
    - "a statistical model for tensor pca"
    - "exact recovery in the stochastic block model"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-8 (induced by source paper "fundamental limits on data acquisition trade offs between sample complexity and query difficulty"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "information recovery from pairwise measurements"
    - "collaborative 20 questions for target localization"
    - "budget optimal task allocation for reliable crowdsourcing systems"

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.IT(Information Theory)