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: "approximate bregman near neighbors in sublinear time beyond the triangle inequality"
  Abstract: In this paper we present the first provable approximate nearest-neighbor (ANN) algorithms for Bregman divergences. Our first algorithm processes queries in O(log^d n) time using O(n log^d n) space and only uses general properties of the underlying distance function (which includes Bregman divergences as a special case). The second algorithm processes queries in O(log n) time using O(n) space and exploits structural constants associated specifically with Bregman divergences. An interesting feature of our algorithms is that they extend the ring-tree + quad-tree paradigm for ANN searching beyond Euclidean distances and metrics of bounded doubling dimension to distances that might not even be symmetric or satisfy a triangle inequality.

Connected through hyperedge HE-1 (induced by source paper "locality sensitive hashing for f divergences mutual information loss and beyond"):
  with members:
    - "categorical feature compression via submodular optimization"
    - "norm ranging lsh for maximum inner product search"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "greedy merge degrading has optimal power law"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "f divergence inequalities"
    - "a survey on learning to hash"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-2 (induced by source paper "permutation search methods are efficient yet faster search is possible"):
  with members:
    - "imagenet large scale visual recognition challenge"
    - "indexability concentration and vc theory"
    - "searching in one billion vectors re rank with source coding"
    - "cophir a test collection for content based image retrieval"

Connected through hyperedge HE-3 (induced by source paper "a directed isoperimetric inequality with application to bregman near neighbor lower bounds"):
  with members:
    - "analysis of boolean functions"
    - "a o n monotonicity tester for boolean functions over the hypercube"
    - "lower bounds on near neighbor search via metric expansion"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "lower bounds on locality sensitive hashing"

Connected through hyperedge HE-4 (induced by source paper "k mle a fast algorithm for learning statistical mixture models"):
  with members:
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "statistical exponential families a digest with flash cards"
    - "k means requires exponentially many iterations even in the plane"
    - "inference and evaluation of the multinomial mixture model for text clustering"

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.CG(Computational Geometry)