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: "tight error bounds for structured prediction"
  Abstract: Structured prediction tasks in machine learning involve the simultaneous prediction of multiple labels. This is typically done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise elements, each depending on two specific labels. Intuitively, the more pairwise terms are used, the better the expected accuracy. However, there is currently no theoretical account of this intuition. This paper takes a significant step in this direction. #R##N#We formulate the problem as classifying the vertices of a known graph $G=(V,E)$, where the vertices and edges of the graph are labelled and correlate semi-randomly with the ground truth. We show that the prospects for achieving low expected Hamming error depend on the structure of the graph $G$ in interesting ways. For example, if $G$ is a very poor expander, like a path, then large expected Hamming error is inevitable. Our main positive result shows that, for a wide class of graphs including 2D grid graphs common in machine vision applications, there is a polynomial-time algorithm with small and information-theoretically near-optimal expected error. Our results provide a first step toward a theoretical justification for the empirical success of the efficient approximate inference algorithms that are used for structured prediction in models where exact inference is intractable.

Connected through hyperedge HE-1 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs"):
  with members:
    - "testing graph clusterability algorithms and lower bounds"
    - "approximate correlation clustering using same cluster queries"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "learning communities in the presence of errors"
    - "testing cluster structure of graphs"
    - "how robust are reconstruction thresholds for community detection"
    - "correlation clustering with noisy partial information"

Connected through hyperedge HE-2 (induced by source paper "information recovery from pairwise measurements"):
  with members:
    - "community recovery in graphs with locality"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "bounds among f divergences"
    - "discrete signal processing on graphs sampling theory"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "asymptotic mutual information for the two groups stochastic block model"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral detection in the censored block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"
    - "near optimal joint object matching via convex relaxation"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-4 (induced by source paper "information recovery from pairwise measurements a shannon theoretic approach"):
  with members:
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "near optimal joint object matching via convex relaxation"
    - "minimax estimation of functionals of discrete distributions"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "renyi divergence and kullback leibler divergence"
    - "exact recovery in the stochastic block model"
    - "haplotype assembly an information theoretic view"

Connected through hyperedge HE-5 (induced by source paper "correlation clustering with noisy partial information"):
  with members:
    - "constant factor approximation for balanced cut in the pie model"
    - "fast planar correlation clustering for image segmentation"
    - "clustering partially observed graphs via convex optimization"

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.LG(Machine Learning)