human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "complexity theoretic limitations on learning halfspaces"):
  Abstract: We study the problem of agnostically learning halfspaces which is defined by a fixed but unknown distribution $\mathcal{D}$ on $\mathbb{Q}^n\times \{\pm 1\}$. We define $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D})$ as the least error of a halfspace classifier for $\mathcal{D}$. A learner who can access $\mathcal{D}$ has to return a hypothesis whose error is small compared to $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D})$. #R##N#Using the recently developed method of the author, Linial and Shalev-Shwartz we prove hardness of learning results under a natural assumption on the complexity of refuting random $K$-$\mathrm{XOR}$ formulas. We show that no efficient learning algorithm has non-trivial worst-case performance even under the guarantees that $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D}) \le \eta$ for arbitrarily small constant $\eta>0$, and that $\mathcal{D}$ is supported in $\{\pm 1\}^n\times \{\pm 1\}$. Namely, even under these favorable conditions its error must be $\ge \frac{1}{2}-\frac{1}{n^c}$ for every $c>0$. In particular, no efficient algorithm can achieve a constant approximation ratio. Under a stronger version of the assumption (where $K$ can be poly-logarithmic in $n$), we can take $\eta = 2^{-\log^{1-\nu}(n)}$ for arbitrarily small $\nu>0$. Interestingly, this is even stronger than the best known lower bounds (Arora et. al. 1993, Feldamn et. al. 2006, Guruswami and Raghavendra 2006) for the case that the learner is restricted to return a halfspace classifier (i.e. proper learning).

Includes members:
  Member "how to refute a random csp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "tensor prediction rademacher complexity and random 3 xor":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "extended formulation lower bounds for refuting random csps")
  Member "complexity theoretic limitations on learning dnf s":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity")
  Member "a ptas for agnostically learning halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "the power of localization for efficiently learning linear separators with noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "from average case complexity to improper learning complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "more data speeds up training time in learning halfspaces over sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "on the complexity of random satisfiability problems with planted solutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion")

Question: Please tell me which class the source paper 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)