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: "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
  Abstract: Empirical risk minimization (ERM) is ubiquitous in machine learning and underlies most supervised learning methods. While there has been a large body of work on algorithms for various ERM problems, the exact computational complexity of ERM is still not understood. We address this issue for multiple popular ERM problems including kernel SVMs, kernel ridge regression, and training the final layer of a neural network. In particular, we give conditional hardness results for these problems based on complexity-theoretic assumptions such as the Strong Exponential Time Hypothesis. Under these assumptions, we show that there are no algorithms that solve the aforementioned ERM problems to high accuracy in sub-quadratic time. We also give similar hardness results for computing the gradient of the empirical loss, which is the main computational burden in many non-convex learning tasks.

Connected through hyperedge HE-1 (induced by source paper "tight kernel query complexity of kernel ridge regression and kernel k means clustering"):
  with members:
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "on learning mixtures of well separated gaussians"
    - "recursive sampling for the nystr om method"
    - "on the complexity of learning with kernels"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "sharp analysis of low rank kernel matrix approximations"
    - "adaptively learning the crowd kernel"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-2 (induced by source paper "more consequences of falsifying seth and the orthogonal vectors conjecture"):
  with members:
    - "conditional lower bounds for all pairs max flow"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "distributed pcp theorems for hardness of approximation in p"
    - "tree edit distance cannot be computed in strongly subcubic time unless apsp can"
    - "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve"
    - "improving viterbi is hard better runtimes imply faster clique algorithms"
    - "on the hardness of partially dynamic graph problems and connections to diameter"
    - "tight hardness results for maximum weight rectangles"

Connected through hyperedge HE-3 (induced by source paper "multi resolution hashing for fast pairwise summations"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "hashing based estimators for kernel density in high dimensions"
    - "stochastic optimization with bandit sampling"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "tight cell probe bounds for succinct boolean matrix vector multiplication"
    - "distance sensitive hashing"
    - "optimal hashing based time space trade offs for approximate near neighbors"

Connected through hyperedge HE-4 (induced by source paper "seth based lower bounds for subset sum and bicriteria path"):
  with members:
    - "a short note on merlin arthur protocols for subset sum"
    - "parameterizing pwm and profile matching and knapsack by the feasible weight solutions count"
    - "subquadratic algorithms for algebraic generalizations of 3sum"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "a dichotomy for regular expression membership testing"
    - "improved bounds for 3sum k sum and linear degeneracy"
    - "clustered integer 3sum via additive combinatorics"
    - "which regular expression patterns are hard to match"

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.CC(Computational Complexity)