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: "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"
  Abstract: We consider a crowdsourcing model in which $n$ workers are asked to rate the quality of $n$ items previously generated by other workers. An unknown set of $\alpha n$ workers generate reliable ratings, while the remaining workers may behave arbitrarily and possibly adversarially. The manager of the experiment can also manually evaluate the quality of a small number of items, and wishes to curate together almost all of the high-quality items with at most an $\epsilon$ fraction of low-quality items. Perhaps surprisingly, we show that this is possible with an amount of work required of the manager, and each worker, that does not scale with $n$: the dataset can be curated with $\tilde{O}\Big(\frac{1}{\beta\alpha^3\epsilon^4}\Big)$ ratings per worker, and $\tilde{O}\Big(\frac{1}{\beta\epsilon^2}\Big)$ ratings by the manager, where $\beta$ is the fraction of high-quality items. Our results extend to the more general setting of peer prediction, including peer grading in online classrooms.

Connected through hyperedge HE-1 (induced by source paper "list decodable subspace recovery"):
  with members:
    - "list decodable linear regression"
    - "recent advances in algorithmic high dimensional robust statistics"
    - "list decodable learning via sum of squares"
    - "outlier robust moment estimation via sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "bayesian estimation from few samples community detection and related problems"
    - "mixture models robustness and sum of squares proofs"

Connected through hyperedge HE-2 (induced by source paper "local statistics semidefinite programming and community detection"):
  with members:
    - "a tight degree 4 sum of squares lower bound for the sherrington kirkpatrick hamiltonian"
    - "vector colorings of random ramanujan and large girth irregular graphs"
    - "computational hardness of certifying bounds on constrained pca problems"
    - "the lov asz theta function for random regular graphs and community detection in the hard regime"
    - "learning from untrusted data"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-3 (induced by source paper "list decodable learning via sum of squares"):
  with members:
    - "list decodable linear regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "outlier robust moment estimation via sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "learning from untrusted data"

Connected through hyperedge HE-4 (induced by source paper "adversarial task assignment"):
  with members:
    - "efficiency of active learning for the allocation of workers on crowdsourced classification tasks"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-5 (induced by source paper "list decodable robust mean estimation and learning mixtures of spherical gaussians"):
  with members:
    - "certified defenses for data poisoning attacks"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "on learning mixtures of well separated gaussians"
    - "learning from untrusted data"

Connected through hyperedge HE-6 (induced by source paper "certified defenses for data poisoning attacks"):
  with members:
    - "poisoning behavioral malware clustering"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "sok security and privacy in machine learning"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "generative poisoning attack method against neural networks"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "learning from untrusted data"

Connected through hyperedge HE-7 (induced by source paper "certified computation in crowdsourcing"):
  with members:
    - "being robust in high dimensions can be practical"
    - "learning from untrusted data"
    - "feature multi selection among subjective features"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanism design for time critical and cost critical task execution via crowdsourcing"
    - "optimal crowdsourcing contests"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-8 (induced by source paper "a data prism semi verified learning in the small alpha regime"):
  with members:
    - "learning from untrusted data"
    - "robust estimators in high dimensions without the computational intractability"
    - "strongly refuting random csps below the spectral threshold"
    - "matrix completion from a few entries"

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.HC(Human-Computer Interaction)