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: "a directed isoperimetric inequality with application to bregman near neighbor lower bounds"
  Abstract: Bregman divergences $D_\phi$ are a class of divergences parametrized by a convex function $\phi$ and include well known distance functions like $\ell_2^2$ and the Kullback-Leibler divergence. There has been extensive research on algorithms for problems like clustering and near neighbor search with respect to Bregman divergences, in all cases, the algorithms depend not just on the data size $n$ and dimensionality $d$, but also on a structure constant $\mu \ge 1$ that depends solely on $\phi$ and can grow without bound independently. #R##N#In this paper, we provide the first evidence that this dependence on $\mu$ might be intrinsic. We focus on the problem of approximate near neighbor search for Bregman divergences. We show that under the cell probe model, any non-adaptive data structure (like locality-sensitive hashing) for $c$-approximate near-neighbor search that admits $r$ probes must use space $\Omega(n^{1 + \frac{\mu}{c r}})$. In contrast, for LSH under $\ell_1$ the best bound is $\Omega(n^{1+\frac{1}{cr}})$. #R##N#Our new tool is a directed variant of the standard boolean noise operator. We show that a generalization of the Bonami-Beckner hypercontractivity inequality exists "in expectation" or upon restriction to certain subsets of the Hamming cube, and that this is sufficient to prove the desired isoperimetric inequality that we use in our data structure lower bound. #R##N#We also present a structural result reducing the Hamming cube to a Bregman cube. This structure allows us to obtain lower bounds for problems under Bregman divergences from their $\ell_1$ analog. In particular, we get a (weaker) lower bound for approximate near neighbor search of the form $\Omega(n^{1 + \frac{1}{cr}})$ for an $r$-query non-adaptive data structure, and new cell probe lower bounds for a number of other near neighbor questions in Bregman space.

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 "hardness of approximate nearest neighbor search"):
  with members:
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal las vegas locality sensitive data structures"
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "the curse of medium dimension for geometric problems in almost every norm"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "a faster subquadratic algorithm for finding outlier correlations"

Connected through hyperedge HE-3 (induced by source paper "distributed pcp theorems for hardness of approximation in p"):
  with members:
    - "fully dynamic maximal matching in constant update time"
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "the curse of medium dimension for geometric problems in almost every norm"
    - "on the hardness of partially dynamic graph problems and connections to diameter"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "design of dynamic algorithms via primal dual method"

Connected through hyperedge HE-4 (induced by source paper "lower bounds on time space trade offs for approximate near neighbors"):
  with members:
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "scalability and total recall with fast coveringlsh"
    - "learning to hash for indexing big data a survey"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "tight lower bounds for data dependent locality sensitive hashing"

Connected through hyperedge HE-5 (induced by source paper "approximate near neighbors for general symmetric norms"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "learning to hash for indexing big data a survey"
    - "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"
    - "streaming symmetric norms via measure concentration"
    - "optimal data dependent hashing for approximate near neighbors"
    - "sketching and embedding are equivalent for norms"
    - "hashing for similarity search a survey"
    - "a unified framework for linear dimensionality reduction in l1"

Connected through hyperedge HE-6 (induced by source paper "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities"):
  with members:
    - "optimal data dependent hashing for approximate near neighbors"
    - "lower bounds on near neighbor search via metric expansion"

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)